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)C)[C@@H](C)CC)[C@@H](C)CC)C(C)C)[C@@H](C)O)C(C)C)C(C)C)CSSC[C@@H](C(=O)N[C@@H](CC2=CC=CC=C2)C(=O)N[C@@H](CO)C(= O)N[C@@H](CC(C)C)C(=O)N2CCC[C@H]2C(=O)O)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CCCCN)NC(=O)[C@H]([C@@H](C)CC)NC(=O)[C@ H](CC2=CC=CC=C2)NC(=O)[C@H](CC2=CNC=N2)NC(=O)[C@H](CO)NC(=O)[C@H](CCSC)NC(=O)[C@H](CC2=CNC=N2)NC(=O)[C@H]([C@@H] (C)O)NC(=O)[C@H](CC2=CC=C(O)C=C2)NC(=O)[C@H]([C@@H](C)CC)NC(=O)[C@H](CC2=CNC=N2)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H] (CC2=CNC=N2)NC(=O)[C@H](C)NC(=O)[C@H]([C@@H](C)O)NC(=O)[C@H](CO)NC(=O)[C@H](CO)NC(=O)[C@H](C)NC1=O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. OCC(O)CO failed sanitization [15:27:56] Explicit valence for atom # 5899 O, 3, is greater than permitted Mol Br. Br. Br. Br. Br. Br. Br. Br. Br. Br. Br. CC[C@H](C)[C@H](NC(=O)CNC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@H](CC(=O)O)N C(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)CNC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CO)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(= O)[C@@H]1CCCN1C(=O)[C@H](C)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CO)NC(=O)[C@H](CC1=CC=CC=C1)NC(= O)[C@@H](NC(=O)[C@H](CO)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CCC(=O)O)NC(=O)[ C@H](CC1=CNC=N1)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](C )NC(=O)[C@@H]1CCCN1C(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)CNC(=O)[C@H](CC (N)=O)NC(=O)[C@H](CO)NC(=O)[C@H](C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@@H]1CCCN1C(=O)[C@H] (CC(C)C)NC(=O)[C@@H](NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@H](CO)NC (=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@@H](NC(=O)[C@H](CC1=CC=C(O)C= C1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CCC(=O)O)NC(=O)CNC(=O)[C@H](CCCCN)N C(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@H](CO)NC(=O)[C@H](CCCCN)NC(=O)[C@@H](NC(=O)[C@@H] 1CCCN1C(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)CNC(=O)[C@@H](NC(=O)[C@H](CC1=CC=CC=C1)NC (=O)[C@H](CCSC)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](C)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC(C)C)NC(=O) [C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CO)NC(=O)CNC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CC= C(O)C=C1)NC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@@H](NC(=O)[C@H](CC( =O)O)NC(=O)[C@H](CC(C)C)NC(=O)CNC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC( =O)[C@H](CCC(N)=O)NC(=O)[C@@H](NC(=O)CNC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H] (CC(C)C)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@@H]( NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C @H](CCC(=O)O)NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC(N)=O)NC(=O)[ | deepchem.pdf |
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O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. OCCO. O CCO. OC[C@H]1CN[C@@H](O)[C@@H](O)[C@@H]1O. OC[C@H]1CN[C@@H](O)[C@@H](O)[C@@H]1O failed sanitization [15:28:01] Explicit valence for atom # 2041 O, 3, is greater than permitted Mol CC(C)C[C@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC(C)C)NC( =O)[C@H](CC(C)C)NC(=O)[C@@H](N)CO)C(=O)N[C@@H](C)C(=O)O. CCCCCCCCCC(O)O. CC[C@H](C)[C@H](NC(=O)[C@H](C)NC(=O)[C@H] (CC(C)C)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CO)NC(=O)[C@H](CC(=O)O)NC(=O)[C @H](CC(=O)O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H ](CCC(=O)O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CCCCN)NC(=O )[C@@H](NC(=O)[C@H](C)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CCCCN)N C(=O)[C@@H]1CCCN1C(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCSC)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CC(=O)O)NC(=O)CNC(=O) [C@H](CC1=CC=CC=C1)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CCCCN)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CO)N C(=O)[C@H](CCCCN)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[ C@@H](NC(=O)[C@H](CCSC)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)CNC(=O)[C@H](CCC(N)=O)NC(=O)CNC(=O)[C@H](CCC(=O)O)NC(=O)[C @H](CO)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)CNC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC( N)=O)NC(=O)[C@H](CCSC)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CO)NC(=O)[C@H](C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCSC)NC(=O) [C@@H](NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[ C@@H](NC(=O)CNC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC (=O)[C@@H](NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(=O)O)N C(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)CNC(=O)[C@@H]1CCCN1C(=O)[C@@H] (NC(=O)[C@H](CO)NC(=O)[C@H](CCCCN)NC(=O)[C@H](C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC (=O)[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H ](NC(=O)[C@H](CO)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CS)NC(=O)CNC(= O)[C@H](CCC(N)=O)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@@H](NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[ C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](N)CCCCN)C(C)C)[C@@H](C)CC)[C@@H](C)CC)C(C)C)C(C)C)[C@@H](C)CC)[C@@H](C)O | deepchem.pdf |
)[C@@H](C)CC)C(C)C)C(C)C)[C@@H](C)O)C(C)C)[C@@H](C)CC)[C@@H](C)CC)[C@@H](C)O)C(C)C)[C@@H](C)CC)[C@@H](C)O)C(C)C) C(=O)N[C@@H](CC1=CC=CC=C1)C(=O)N[C@H](C(=O)N[C@@H](C)C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@@H](CC(C)C)C(= O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N1CCC[C@H]1C(=O)NCC(=O)N[C@@H](CC(C)C)C(=O )N[C@@H](CC(C)C)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@H](C(=O)N[C@@H](CCCCN)C(=O)N1CCC[C@H]1C(=O)N[C@H](C(=O)N[C@@H](CCC (=O)O)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@H](C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@ @H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)O)C( =O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H]( CC(N)=O)C(=O)N[C@@H](CC1=CNC=N1)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CO)C(=O)N[C@@H](CO)C(=O)N[C@@ H](CCC(N)=O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC1=CC=CC=C1)C(=O)N[C@@H](C)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(C)C) C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCSC)C(=O)N[C@H](C(=O)N[C@@H](CC(=O)O) C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@@ H](CCC(=O)O)C(=O)N[C@@H](CC1=CNC=N1)C(=O)N[C@H](C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C( =O)N[C@@H](CCC(N)=O)C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCCCN)C(=O)N[C@H](C(=O)N[C@@H](CCC(= O)O)C(=O)N[C@H](C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC1=CNC= N1)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@ H](C(=O)N[C@@H](CC1=CC=C(O)C=C1)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(=O)O)C1=OOC(=O)[C@H](CC(C)C)N1)[C@@H](C)CC)[C @@H](C)O)[C@@H](C)O)[C@@H](C)CC)C(C)C)C(C)C)[C@@H](C)O)C(C)C)[C@@H](C)CC)[C@@H](C)O)[C@@H](C)CC)[C@@H](C)CC)C(C) C)[C@@H](C)CC)[C@@H](C)CC)C(C)C)[C@@H](C)CC. CC[C@H](C)[C@H](NC(=O)[C@H](C)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](C)N C(=O)[C@H](CCCCN)NC(=O)[C@H](C)NC(=O)[C@H](CCCCN)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CC 1=CC=CC=C1)NC(=O)[C@H](CO)NC(=O)[C@H](CCCCN)NC(=O)[C@@H](NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CO)NC(=O)[C@H]( CC(=O)O)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CCCCN)NC(=O)[C@H](C)NC (=O)[C@H](CC(C)C)NC(=O)[C@H](C)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](C)NC(=O )[C@@H](N)CO)[C@@H](C)CC)[C@@H](C)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@H](C(=O)NCC(=O)N[C@@H](CCCCN)C(=O)N[C@H](C(=O)N [C@H](C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CO)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CC1=CC=CC=C1)C(=O)N[C @H](C(=O)N[C@H](C(=O)N[C@@H](CC1=CC=C(O)C=C1)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CC(N)=O)C(=O)N[ C@@H](CO)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CCSC)C(=O)NCC(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CC(=O )O)C(=O)N[C@@H](CCCCN)C(=O)N[C@H](C=O)[C@@H](C)CC)[C@@H](C)CC)C(C)C)[C@@H](C)O)[C@@H](C)O)[C@@H](C)O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O failed sanitization [15:28:18] Explicit valence for atom # 3573 O, 3, is greater than permitted Mol CC(O)O. 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CC[C@H](C)[C@H](NC(=O)CNC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H]1CCCN1C(=O)[C@@H](NC(=O)[C@@H]( NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)CNC(=O)[C@H](CCSC)NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@ H](CCCCN)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCC(=O)O)NC( =O)[C@@H](NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@@H](N)CCCCN)C(C)C)C(C)C)[C@@H](C)O)[C@@H ](C)CC)C(C)C)C(=O)N[C@@H](CC1=CC=C(O)C=C1)C(=O)N[C@@H](CC1=CNC2=C1C=CC=C2)C(=O)N[C@H](C(=O)N[C@@H](CC(C)C)C(=O)N [C@@H](CCCCN)C(=O)N[C@@H](CCCCN)C(=O)N[C@H](C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCC(=O)O)C(=O) N[C@@H](CC1=CC=C(O)C=C1)C(=O)N[C@@H](CC(N)=O)C(=O)N1CCC[C@H]1C(=O)N1CCC[C@H]1C(=O)N[C@@H](CCC(O)=ONCCCC[C@H](NC( | deepchem.pdf |
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[Ca H2] failed sanitization [15:28:21] Explicit valence for atom # 2034 C, 5, is greater than permitted Mol CC[C@H](C)[C@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)CNC(=O )CNC(=O)[C@@H]1CCCN1C(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O) [C@@H](NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](C)NC(=O)[C@H](CCCNC(=N)N)NC(=O) [C@H](CCC(N)=O)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CO)NC(=O)[C@H](C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(=O)O)NC(= O)[C@@H]1CCCN1C(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CS)NC (=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](C)NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](C)NC(=O)[C@H](CC (=O)O)NC(=O)[C@H](C)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CC(C)C)NC(=O)[C@@H]1CCCN1C(=O)[C@H](C)NC(=O)[C@@H]1CCCN1C(=O)[C @H](CO)NC(=O)CNC(=O)[C@H](C)NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CCC(N)=O)NC(=O)[C@@H]1CCCN1C( =O)[C@H](CCC(N)=O)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C @H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCCCN)NC(=O)[C@@H](NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H] (C)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCC(=O)O)NC (=O)[C@H](CCCCN)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@H](CS)NC(=O)[C@ H](CCC(=O)O)NC(=O)CNC(=O)[C@H](CCCCN)NC(=O)CNC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CS) NC(=O)[C@H](C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CO)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H] (CCSC)NC(=O)[C@H](CO)NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](C)N C(=O)CNC(=O)[C@@H]1CCCN1C(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CC(C)C)NC(=O)[ C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@H](CCCNC(=N)N)NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCC(N) =O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](C C1=CC=CC=C1)NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](CCSC)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(= O)[C@H](CCC(=O)O)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CO)NC(=O)[C@H](CS)NC(=O)[C@H](C)NC( =O)[C@H](CO)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@@H]( NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)CNC(=O)[C@H](CO)NC(=O)CNC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](NC(=O)[ C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@H](CCCN1NC1=N)NC(=O)CNC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)CN C(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@@H](NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](C)NC(=O)[C@H](C C(C)C)NC(=O)[C@H](CS)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC1=CNC2=C1C=CC=C2) NC(=O)[C@@H]1CCCN1C(=O)CNC(=O)[C@@H](NC(=O)CNC(=O)[C@H](CC(N)=O)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CC(N)=O)NC(=O)[C@H] (CS)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(=O)O)NC(=O)CNC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H]1CCCN1C(=O)[C@@H]1CCCN1C(=O )[C@H](C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H]( CC(C)C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](C)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CCC(=O)O)NC (=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](C)NC( =O)[C@H](CO)NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CCC(N)=O)NC(=O)CNC(=O)[C@@H]1CCCN 1C(=O)[C@H](C)NC(=O)[C@H](CO)NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H]1CCCN1)C(C)C)C(C)C)[C@@H](C)O)[C@@H](C)O)C(C)C)[C@@ H](C)O)[C@@H](C)CC)[C@@H](C)CC)[C@@H](C)O)C(C)C)[C@@H](C)CC)[C@@H](C)O)[C@@H](C)O)C(C)C)[C@@H](C)CC)C(C)C)C(C)C) [C@@H](C)CC)C(C)C)C(C)C)C(C)C)[C@@H](C)CC)[C@@H](C)O)[C@@H](C)O)C(=O)NCC(=O)N[C@@H](C)C(=O)N[C@@H](CC(C)C)C(=O)N [C@@H](CCC(=O)O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC1=CNC2=C1C=CC=C2)C(=O)N[C@@H](CC1=CC=C(O)C= C1)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)NCC(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N [C@@H](CC(C)C)C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@H](C(=O)N1CCC[C@H]1C(=O)N[C@H](C(=O)N[C@@H](CO)C(=O)N[C@@H](CCC(= O)O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@H](C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCC(=O)O)C(=O)N [C@@H](C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@H](C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](CC1=CC=C(O) C=C1)C(=O)N[C@@H](CCCCN)C(=O)N[C@H](C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H ](CCCNC(=N)N)C(=O)N[C@H](C(=O)N[C@@H](CC1=CC=C(O)C=C1)C(=O)N[C@H](C(=O)N[C@@H](CCSC)C(=O)N1CCC[C@H]1C(=O)N[C@@H] (C)C(=O)N[C@@H](CC1=CNC=N1)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@H](C(=O)NCC(=O)N[C@H](C(=O)N[C@@H] (CC(=O)O)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@H](C(=O)N[C@@H](CCCCN)C(=O)NCC(=O)N[C@H](C(=O)N[C@@H](CC1=CC=CC=C1)C(=O)N | deepchem.pdf |
[C@@H](CC1=CC=CC=C1)C(=O)N[C@@H](C)C(=O)N[C@@H](CC1=CNC2=C1C=CC=C2)C(=O)N[C@@H](C)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C @@H](CCCCN)C(=O)N[C@H](C(=O)O)C(C)C)C(C)C)C(C)C)C(C)C)[C@@H](C)O)[C@@H](C)CC)[C@@H](C)O)C(C)C)C(C)C)C(C)C)C(C)C) C(C)C)C(C)C)[C@@H](C)O. CC[C@H](C)[C@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1= CNC=N1)NC(=O)CNC(=O)CNC(=O)[C@@H]1CCCN1C(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H]( NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](C)NC(=O)[C@H ](CCCNC(=N)N)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CO)NC(=O)[C@H](C)NC(=O)[C@H](CC(C)C)NC(=O )[C@H](CC(=O)O)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC(C) C)NC(=O)[C@H](CS)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](C)NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@ H](C)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](C)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CC(C)C)NC(=O)[C@@H]1CCCN1C(=O)[C@H](C)NC(=O) [C@@H]1CCCN1C(=O)[C@H](CO)NC(=O)CNC(=O)[C@H](C)NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CCC(N)=O)N C(=O)[C@@H]1CCCN1C(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](NC(=O) [C@@H]1CCCN1C(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCCCN)NC(=O)[C@@H](NC(=O)[C@H](CCC NC(=N)N)NC(=O)[C@H](C)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CCCNC(=N)N)NC(= O)[C@H](CCC(=O)O)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O )[C@H](CS)NC(=O)[C@H](CCC(=O)O)NC(=O)CNC(=O)[C@H](CCCCN)NC(=O)CNC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](CC( C)C)NC(=O)[C@H](CS)NC(=O)[C@H](C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CO)NC(=O)[C@H](CC1=CC=C (O)C=C1)NC(=O)[C@H](CCSC)NC(=O)[C@H](CO)NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC1=CC=CC =C1)NC(=O)[C@H](C)NC(=O)CNC(=O)[C@@H]1CCCN1C(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCC(N)=O)NC(=O)[ C@H](CC(C)C)NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@H](CCCNC(=N)N)NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCC(=O)O 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[C@@H](CC(=O)O)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@H](C(=O)N[C@@H](CCCCN)C(=O)NCC(=O)N[C@H](C(=O)N[C@@H](CC1=CC=CC=C1) C(=O)N[C@@H](CC1=CC=CC=C1)C(=O)N[C@@H](C)C(=O)N[C@@H](CC1=CNC2=C1C=CC=C2)C(=O)N[C@@H](C)C(=O)N[C@@H](CCC(N)=O)C( =O)N[C@@H](CCCCN)C(=O)N[C@H](C(=O)NCC(=O)N[C@H]1CC(C)C2OC21=O)C(C)C)C(C)C)C(C)C)C(C)C)[C@@H](C)O)[C@@H](C)CC)[C@ @H](C)O)C(C)C)C(C)C)C(C)C)C(C)C)C(C)C)C(C)C)[C@@H](C)O. NC1NCNC2C1NCN2[C@@H]1O[C@H](CSCC[C@H](N)C(O)O)[C@@H](O)[C @H]1O. NC1NCNC2C1NCN2[C@@H]1O[C@H](CSCC[C@H](N)C(O)O)[C@@H](O)[C@H]1O. NS(O)(O)C1CC[C@@H]2CCNC[C@H]2C1. NS(O)(O)C1C C[C@@H]2CCNC[C@H]2C1. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O failed sanitization [15:28:33] Explicit valence for atom # 2206 O, 3, is greater than permitted Mol CC[C@H](C)[C@H](NC(=O)CNC(=O)[C@@H]1CCCN1C(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CO)NC(=O)[C @H](CCSC)NC(=O)CNC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[ C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)CNC(=O)[C@H](CC1=CC=CC=C1)NC(=O )[C@H](C)NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CO)NC(=O)[C@H](CCCCN)NC(=O) [C@@H](NC(=O)[C@H](CO)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC1=CC=C(O)C=C1 )NC(=O)[C@H](C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CO)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H]1CCCN1C(=O)[C@H](C)NC(=O)[C@@H ](NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@H](CCCCN)NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@H](CC1=CC=CC=C1)NC(=O) [C@H](CCCCN)NC(=O)[C@H](C)NC(=O)CNC(=O)[C@H](C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](C)NC(=O)[C@@H](N=C(O)[C@H](CC1 CCC(O[PH](O)(O)O)CC1)NC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(=O)CNC(=O)[C@@H](NC(=O)[C@H](CCSC)NC(=O)[C@H](CC(C)C)NC (=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CO)NC(=O)[C@H](CC(C)C)NC(=O)CNC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CC(=O)O)NC(=O )[C@H](C)NC(=O)[C@@H](NC(=O)[C@H](CCCCN)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CC(N) =O)NC(=O)[C@H](CCC(=O)O)NC(=O)CNC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CS)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC CNC(=N)N)NC(=O)[C@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](C C1=CNC=N1)NC(=O)[C@@H](NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CCCCN)NC(=O)[C @H](CCC(=O)O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCSC)NC(=O)[C@H](C) NC(=O)[C@H](CO)NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CCC(N)=O)NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@H](CCSC)NC(= O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@H ](CC(N)=O)NC(=O)[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(N)=O)NC(= O)[C@H](CS)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H ](CC(=O)O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)CNC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@ @H](NC(=O)[C@H](CCSC)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H](NC(=O) [C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@@H]1CCCN1C(=O)[C@@H]1CCCN1C(=O)[C@H](CCC(=O)O)NC(=O)[C@H ](CCCNC(=N)N)NC(=O)[C@@H](NC(=O)[C@H](CS)NC(=O)[C@@H](NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](C CC(N)=O)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H]( CCCCN)NC(=O)[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CCSC)NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@H ](C)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CCC(=O)O)NC(= O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCSC)NC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@H] (CCC(=O)O)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@H](CCCCN)NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[ C@@H](NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CO)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CCCCN)NC(=O)[C@H](C CCCN)NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@@H](NC(=O)CNC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[ C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)CNC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CCC(N)=O)NC(=O)CNC(=O)CNC(=O)CNC(=O)[C | deepchem.pdf |
@H](CC(C)C)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CCSC)NC(=O)[C@@H](NC(=O)[C@@H] (NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCSC)NC(=O)[C@H](CCC(= O)O)NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@@H](N)CCCCN)[C@@H](C)O)[C@@H](C)CC)[C@@H](C)O)C(C)C)C(C)C)[C@@H](C)O )C(C)C)C(C)C)[C@@H](C)O)[C@@H](C)O)C(C)C)C(C)C)[C@@H](C)CC)C(C)C)C(C)C)[C@@H](C)O)[C@@H](C)CC)[C@@H](C)CC)[C@@H] (C)CC)[C@@H](C)O)C(C)C)C(C)C)C(C)C)[C@@H](C)O)[C@@H](C)CC)[C@@H](C)CC)C(C)C)C(C)C)C(C)C)[C@@H](C)O)[C@@H](C)O)[C @@H](C)O)[C@@H](C)CC)[C@@H](C)O)[C@@H](C)CC)C(C)C)C(C)C)[C@@H](C)CC)[C@@H](C)O)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H] (CC(C)C)C(=O)N[C@@H](CO)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@H](C(=O)N[C@@H](CC1=CC=C(O)C=C1)C(=O)N[C@@H](CCC(=O)O)C(= O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H]( CC1=CC=C(O)C=C1)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N1C CC[C@H]1C(=O)N[C@@H](CCC(=O)O)C(=O)NCC(=O)N[C@@H](CS)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCCCN)C( =O)N[C@H](C(=O)N[C@@H](CC1=CC=C(O)C=C1)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCSC)C(=O)N[C@@H]( CCCNC(=N)N)C(=O)N[C@@H](C)C(=O)N[C@@H](CS)C(=O)N[C@@H](CC1=CNC2=C1C=CC=C2)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CC1 =CNC2=C1C=CC=C2)C(=O)N[C@@H](CC(N)=O)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CO)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCCNC(=N) N)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CO)C(=O)N[C@@H](CC1=CC=CC=C1)C(=O)N[C@@H](C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@H](C(= O)N[C@@H](CC1=CNC=N1)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](C)C(=O)N[C@@H](CC1=CC=CC=C1)C(=O)N[C@@H](CCC(=O)O)C(=O)N [C@H](C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CC1=CC=CC=C1)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CO)C( =O)N[C@@H](CO)C(=O)N[C@H](C1=O[C@@](CO)(C2=OO2)N1)[C@@H](C)CC)[C@@H](C)O)[C@@H](C)CC)C(C)C)C(C)C. 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CCC[C@H]1C(=O)N[C@@H](CCC(=O)O)C(=O)NCC(=O)N[C@@H](CS)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCCCN)C (=O)N[C@H](C(=O)N[C@@H](CC1=CC=C(O)C=C1)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCSC)C(=O)N[C@@H] (CCCNC(=N)N)C(=O)N[C@@H](C)C(=O)N[C@@H](CS)C(=O)N[C@@H](CC1=CNC2=C1C=CC=C2)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CC 1=CNC2=C1C=CC=C2)C(=O)N[C@@H](CC(N)=O)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CO)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCCNC(=N )N)C(=O)N1CCC[C@H]1C(=O)N[C@@H](CO)C(=O)N[C@@H](CC1=CC=CC=C1)C(=O)N[C@@H](C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@H](C( =O)N[C@@H](CC1=CNC=N1)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](C)C(=O)N[C@@H](CC1=CC=CC=C1)C(=O)N[C@@H](CCC(=O)O)C(=O) N[C@H](C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CC1=CC=CC=C1)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CO)C (=O)N[C@@H](CO)C(=O)N[C@H](C(=O)N[C@]12O=C13O1C2O13)[C@@H](C)CC)[C@@H](C)O)[C@@H](C)CC)C(C)C)C(C)C. CO[C@H](C1CCC CC1)C(O)N1CC2NNC(NC(O)C3CCC(N4CCN(C)CC4)CC3)C2C1. CO[C@H](C1CCCCC1)C(O)N1CC2NNC(NC(O)C3CCC(N4CCN(C)CC4)CC3)C2C1. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. [Mg H2 ]. [Mg H2] failed sanitization [15:28:45] Explicit valence for atom # 21 C, 5, is greater than permitted Mol [H]ON([H])C1([H])([H])N([H])[C@@]([H])(C([H])([H])O[H])[C@]([H])(O[C@]2([H])O[C@]([H])(C([H])([H])O[H])[C@@] ([H])(O[C@]3([H])O[C@@]([H])(C([H])([H])O[H])[C@]([H])(OC([H])([H])[H])[C@@]([H])(O[H])[C@]3([H])O[H])[C@]([H])( O[H])[C@]2([H])O[H])[C@@]([H])(O[H])[C@@]1([H])O[H] failed sanitization [15:28:52] Explicit valence for atom # 4107 O, 3, is greater than permitted Mol CC[C@H](C)[C@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)CNC(=O )CNC(=O)[C@@H]1CCCN1C(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O) [C@@H](NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](C)NC(=O)[C@H](CCCNC(=N)N)NC(=O) [C@H](CCC(N)=O)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CO)NC(=O)[C@H](C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(=O)O)NC(= O)[C@@H]1CCCN1C(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CS)NC (=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](C)NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](C)NC(=O)[C@H](CC (=O)O)NC(=O)[C@H](C)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CC(C)C)NC(=O)[C@@H]1CCCN1C(=O)[C@H](C)NC(=O)[C@@H]1CCCN1C(=O)[C @H](CO)NC(=O)CNC(=O)[C@H](C)NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CCC(N)=O)NC(=O)[C@@H]1CCCN1C( | deepchem.pdf |
=O)[C@H](CCC(N)=O)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C @H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCCCN)NC(=O)[C@@H](NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H] (C)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCC(=O)O)NC (=O)[C@H](CCCCN)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@@H]1CSSC[C@H](N C(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(=O)O)NC(=O)CNC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H]2CCCN2C(=O)[C@@H]2CCCN2C(=O)[C@H ](C)NC(=O)[C@H](CC2=CC=C(O)C=C2)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(C) C)NC(=O)[C@H](CC2=CC=C(O)C=C2)NC(=O)[C@H](C)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H]2CCCN2C(=O)[C@H](CCC(=O)O)NC(=O)[ C@H](CC2=CC=CC=C2)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CC2=CC=C(O)C=C2)NC(=O)[C@H](C)NC(=O)[C 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C@@H](CC(C)C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CS)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@@H](CCC(N)=O)C( =O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CC2=CC=CC=C2)C(=O)N[C@@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCC(=O)N[C@@H](CCC(= O)O)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CC(C) C)C(=O)N[C@@H]([C@@H](C)CC)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H]([C@@H](C)CC)C(=O)NCC(=O)N[C@@H](CO)C(=O)NCC(=O)N2CC C[C@H]2C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CC2=CC=C(O)C=C2)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H ](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CO)C(=O)N[C@@H](C)C(=O)N[C@@H](CS)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC2=CNC =N2)C(=O)N[C@@H](CC2=CC=CC=C2)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H]([C@@H](C)CC)C(=O)N[C@@H]([ C@@H](C)O)C(=O)N[C@@H](CCSC)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC2=CC=CC=C2)C(=O)N[C@@H]( CC(C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCC(N) =O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC2=CNC3=C2C=CC=C3)C(=O )N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCC(=O)O)C(=O)N2CCC[C@H]2C(=O)NCC(=O)N[ C@@H](C)C(=O)N[C@@H](CC2=CC=CC=C2)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](CC2=CNC3=C2C=CC=C3)C(=O)N[C@@H](CO)C(=O)N[C@ @H](CCSC)C(=O)N[C@@H](CC2=CC=C(O)C=C2)C(=O)N[C@@H](CO)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CC2=CNC=N2)C(=O)N[C@@H] (C)C(=O)N[C@@H](CS)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H]([C@@H](C)CC)C(=O)N[C@@H](CCC(=O)O)C(=O)NCC(=O)N[C@@H](CCCCN) C(=O)NCC(=O)N[C@@H](CCC(=O)O)C(=O)N1)C(C)C)C(C)C)[C@@H](C)CC)C(C)C)C(C)C)C(C)C)[C@@H](C)CC)[C@@H](C)O)[C@@H](C)O )C(=O)NCC(=O)N[C@@H](C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CO)C(=O)N[C@ @H](CC1=CNC2=C1C=CC=C2)C(=O)N[C@@H](CC1=CC=C(O)C=C1)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)NCC(=O)N[C@@H](CCC(= O)O)C(=O)N[C@@H](C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC(C)C)C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@H](C(=O)N1CCC[C@ H]1C(=O)N[C@H](C(=O)N[C@@H](CO)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@H](C(= O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@H](C(=O)N[C@@H](CCCNC(=N) N)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](CC1=CC=C(O)C=C1)C(=O)N[C@@H](CCCCN)C(=O)N[C@H](C(=O)N[C@@H](CCCNC(=N)N)C(= O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@H](C(=O)N[C@@H](CC1=CC=C(O)C=C1)C(=O)N[C@ H](C(=O)N[C@@H](CCSC)C(=O)N1CCC[C@H]1C(=O)N[C@@H](C)C(=O)N[C@@H](CC1=CNC=N1)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC (N)=O)C(=O)N[C@H](C(=O)NCC(=O)N[C@H](C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@H](C(=O)N[C@@H](CCCCN)C( =O)NCC(=O)N[C@H](C(=O)N[C@@H](CC1=CC=CC=C1)C(=O)N[C@@H](CC1=CC=CC=C1)C(=O)N[C@@H](C)C(=O)N[C@@H](CC1=CNC2=C1C=CC =C2)C(=O)N[C@@H](C)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CCCCN)C(=O)N[C@H](C(=O)O)C(C)C)C(C)C)C(C)C)C(C)C)[C@@H](C) O)[C@@H](C)CC)[C@@H](C)O)C(C)C)C(C)C)C(C)C)C(C)C)C(C)C)C(C)C)[C@@H](C)O. C[S@@H](CC[C@H](N)C(O)O)C[C@H]1O[C@@H](N | deepchem.pdf |
2CNC3C(N)NCNC32)[C@H](O)[C@@H]1O. C[S@@H](CC[C@H](N)C(O)O)C[C@H]1O[C@@H](N2CNC3C(N)NCNC32)[C@H](O)[C@@H]1O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. OC[C@H]1CC2CCC(N(O)O)CC2CN1. OC[C@H]1CC2CC[C@@H](N(O)O)CC2CN1. OC[C@H] 1C[C@H]2CC[C@@H](N(O)O)CC2CN1. O[PH](O)(O)O failed sanitization [15:29:06] Explicit valence for atom # 1 C, 5, is greater than permitted Mol [H]ON([H])C([H])([H])(N([H])[H])N([H])C([H])([H])C([H])([H])[C@@]([H])(C([H])(O[H])O[H])[N+]([H])([H])[H] fa iled sanitization [15:29:09] Explicit valence for atom # 391 O, 4, is greater than permitted Mol CC[C@@H]1C[C@]1(N[C@@H](O)[C@@H]1C[C@@H]2CN1[C@@H](O)[C@H](C(C)(C)C)NC(O)OCC(C)(C)CCCC[C@@H]1CCCC3CN(CC31)C( O)O2)C(O)NS(O)(O)C1CC1. CC[C@H](C)[C@H](NC(=O)CNC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)CNC(=O)[C@ H](C)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CS)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H]1CCCN1C(=O)CNC(=O)CNC(=O) [C@H](C)NC(=O)[C@H](CO)NC(=O)CNC(=O)[C@H](CCCCN)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CO)NC (=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CO)NC(=O)[C@H](CC(C)C)NC(=O)[C@H ](CC(C)C)NC(=O)[C@H](CO)NC(=O)CNC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CO)NC(=O)[C@H](CC(=O)O)NC(=O)CNC(=O)[C@H](CCCN C(=N)N)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@@H](NC(=O)[C@@H](NC( =O)[C@H](CC(=O)O)NC(=O)[C@H](C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@ H](CC(C)C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CO)NC(=O)[C@H](CO)NC(=O )CNC(=O)[C@H](CS)NC(=O)[C@@H](NC(=O)[C@H](CS)NC(=O)[C@@H]1CCCN1C(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CO)NC( =O)[C@@H]1CCCN2C3(=N)NO23C(=O)CC[C@H](NC(=O)CNC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H]( CC(N)=O)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)CNC(=O)[C@@H](NC(=O)[C@H](CCC(N)=O)NC (=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CS)NC(=O)CNC(=O)[C@H](CCC(=O)O)N C(=O)[C@H](CCC(=O)O)NC(=O)CNC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H](NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CCC(N)=O)NC(=O)[ C@H](C)NC(=O)[C@H](CC2=CC=C(O)C=C2)NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(=O)CNC(=O)[C@H](CO)NC(=O)[C @H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)[C@H](CCCNC(=N)N)NC(=O)CNC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H] (NC(=O)[C@@H](NC(=O)[C@H](CO)NC(=O)CNC(=O)[C@H](CCCCN)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CCSC)NC(=O )[C@H](CO)NC(=O)[C@H](C)NC(=O)[C@H](CCSC)NC(=O)[C@H](CC2=CNC=N2)NC(=O)[C@H](CO)NC(=O)CN)C(C)C)C(C)C)[C@@H](C)CC) C(C)C)[C@@H](C)CC)[C@@H](C)O)[C@@H](C)O)[C@@H](C)O)[C@@H](C)O)C(C)C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCC(N)=O)C(= O)N[C@@H]([C@@H](C)CC)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CO)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](C)C(=O)N[C@@H]([C@ @H](C)O)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CC2=CC=CC=C2)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H] (C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CO)C(=O)N[C@@H]([C@@H](C)CC)C(=O)N[C@@H](CC(N)=O)C(=O)NCC(=O)N[C@@H](C(C )C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC2=CNC3=C2C=CC=C3)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](C C2=CC=C(O)C=C2)C(=O)N[C@@H](CC2=CNC=N2)C(=O)NCC(=O)N[C@@H](C)C(=O)NCC(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CCCNC(= N)N)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H]([C@@H](C)CC)C(=O)N[C@@H](C)C(=O)N[C@@H](CO)C(=O)N2CCC[C@H]2C(=O)N[C@@H] (CCCCN)C(=O)NCC(=O)N2CCC[C@H]2C(=O)N[C@@H](C(C)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CCSC )C(=O)N[C@@H](CC2=CC=C(O)C=C2)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CC(=O )O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C(C)C)C(=O)NCC(=O)N[C@@H](CC2=CNC3=C 2C=CC=C3)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](C)C(=O)N2CCC[C@H]2C(=O)N[C@@H](CCC(N)=O)C(=O)NCC(=O)N[C@@H](CO)C(=O) N1)[C@@H](C)O)[C@@H](C)O)C(C)C)[C@@H](C)O)C(C)C)[C@@H](C)CC)C(C)C)[C@@H](C)CC)C(C)C)C(=O)N[C@@H](CC1=CC=CC=C1)C( =O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](C)C(=O)N[C@@H](C)C(=O)N[C@H](C(=O)N[C@@H](CO)C(=O)N[C@H](C(=O)N[C@@H](CCC1N2C (=N)N12)C(=O)NCC(=O)N[C@H](C(=O)N[C@@H](C)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](C)C(=O)N[C@H](C(=O)N[C@@H](CC(=O)O)C(= O)N[C@@H](CC1=CC=CC=C1)C(=O)N[C@H](C(=O)N1CCC[C@H]1C(=O)N[C@H](C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CO)C(=O)N[C@@H ](CC(C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CCC12NC1(=N)N2)C1=OO1)[C@@ H](C)O)[C@@H](C)O)C(C)C)[C@@H](C)CC)C(C)C)C(C)C)[C@@H](C)O)C(C)C. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. OS(O)(O)O. OS(O)(O)O. [Zn] failed sanitization [15:29:42] Explicit valence for atom # 164 O, 3, is greater than permitted Mol CC[C@@H]1C[C@]1(NC(O)[C@@H]1C[C@@H]2CN1[C@@H](O)[C@H](C(C)(C)C)N[C@H](O)OCC(C)(C)CCCC[C@H]1CCCC3CN(CC31)C(O) O2)[C@@H](O)NS(O)(O)C1CC1. CC[C@H](C)[C@H](NC(=O)CNC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)CNC(=O) [C@H](C)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CS)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H]1CCCN1C(=O)CNC(=O)CNC( =O)[C@H](C)NC(=O)[C@H](CO)NC(=O)CNC(=O)[C@H](CCCCN)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CO )NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CO)NC(=O)[C@H](CC(C)C)NC(=O)[ C@H](CC(C)C)NC(=O)[C@H](CO)NC(=O)CNC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CO)NC(=O)[C@H](CC(=O)O)NC(=O)CNC(=O)[C@H](C CCNC(=N)N)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@@H](NC(=O)[C@@H]( NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O) [C@H](CC(C)C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CO)NC(=O)[C@H](CO)NC (=O)CNC(=O)[C@H](CS)NC(=O)[C@@H](NC(=O)[C@H](CS)NC(=O)[C@@H]1CCCN1C(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CO) NC(=O)[C@@H]1CCCNC2(=NN2)OC(=O)CC[C@H](NC(=O)CNC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H] (CC(N)=O)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)CNC(=O)[C@@H](NC(=O)[C@@H]2CCC(N)=OO =C([C@H](CCC(=O)O)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CS)NC(=O)CNC(=O)[C@H](CC3OC3=O)NC(=O)[C@@H](NC(=O)CNC(=O)[C@H ](CCCNC(=N)N)NC(=O)[C@@H](NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](C)NC(=O)[C@H](CC3=CC=C(O)C=C3)NC (=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@@H]3CC(O)=ON3C(=O)CNC(=O)[C@H](CO)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O )[C@@H](NC(=O)[C@H](CCCNC(=N)N)NC(=O)CNC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](CO)NC(=O)C NC(=O)[C@H](CCCCN)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CCSC)NC(=O)[C@H](CO)NC(=O)[C@H](C)NC(=O)[C@H]( CCSC)NC(=O)[C@H](CC3=CNC=N3)NC(=O)[C@@H](N)CO)C(C)C)C(C)C)[C@@H](C)CC)C(C)C)[C@@H](C)CC)[C@@H](C)O)[C@@H](C)O)C3 C4C(O)=O43)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CO)C(=O)N2)[C@@H](C)O)C(C)C)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CCC(N)=O) C(=O)N[C@@H]([C@@H](C)CC)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H](CO)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](C)C(=O)N[C@@H]( [C@@H](C)O)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CC2=CC=CC=C2)C(=O)N[C@@H](CC(C)C)C(=O)N[C@ @H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CO)C(=O)N[C@@H]([C@@H](C)CC)C(=O)N[C@@H](CC(N)=O)C(=O)NCC(=O)N[C@@H]( C(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC2=CNC3=C2C=CC=C3)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H ](CC2=CC=C(O)C=C2)C(=O)N[C@@H](CC2=CNC=N2)C(=O)NCC(=O)N[C@@H](C)C(=O)NCC(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CCCN C(=N)N)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H]([C@@H](C)CC)C(=O)N[C@@H](C)C(=O)N[C@@H](CO)C(=O)N2CCC[C@H]2C(=O)N[C@ @H](CCC2CN2)C(=O)NCC(=O)N2CCC[C@H]2C(=O)N[C@@H](C(C)C)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H] (CCSC)C(=O)N[C@@H](CC2=CC=C(O)C=C2)C(=O)N[C@@H]([C@@H](C)O)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](C(C)C)C(=O)N[C@@H]( CC(=O)O)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C(C)C)C(=O)NCC(=O)N[C@@H](CC2=C NC3=C2C=CC=C3)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](C)C(=O)N2CCC[C@H]2C(=O)N[C@@H](CCC(N)=O)C(=O)NCC(=O)N[C@@H](CO) C(=O)N1)[C@@H](C)O)[C@@H](C)O)C(C)C)[C@@H](C)O)C(C)C)[C@@H](C)CC)C(C)C)[C@@H](C)CC)C(C)C)C(=O)N[C@@H](CC1=CC=CC= C1)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](C)C(=O)N[C@@H](C)C(=O)N[C@H](C(=O)N[C@@H](CO)C(=O)N[C@H](C(=O)N[C@@H](CC CNC(=N)N)C(=O)NCC(=O)N[C@H](C(=O)N[C@@H](C)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](C)C(=O)N[C@H](C(=O)N[C@@H](C)C(=O)N[C @@H](CC1=CC=CC=C1)C(=O)N[C@H](C(=O)N1CCC[C@H]1C(=O)N[C@H](C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CO)C(=O)N[C@@H](CC( C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CO)C1=O O1)[C@@H](C)O)[C@@H](C)O)C(C)C)[C@@H](C)CC)C(C)C)C(C)C)[C@@H](C)O)C(C)C. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. | deepchem.pdf |
O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. OS(O)(O)O. OS(O)( O)O. OS(O)(O)O. OS(O)(O)O. [Zn] failed sanitization [15:30:26] Explicit valence for atom # 1064 O, 3, is greater than permitted Mol CC[C@H](C)[C@H](NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)CNC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)CNC(=O)[C@@H]( NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@H](CO)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O) [C@H](CO)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@ H](CCC(=O)O)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H](NC(=O) [C@H](CC(N)=O)NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H]( NC(=O)[C@H](CC(C)C)NC(=O)[C@@H]1CCCN1C(=O)[C@H](C)NC(=O)[C@H](CO)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCC(=O)O)NC(=O)[ C@H](C)NC(=O)[C@H](CS)NC(=O)[C@H](C)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](C) NC(=O)[C@@H](NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)CNC(=O)CNC (=O)[C@H](C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@H](CCC(=O )O)NC(=O)[C@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H]( CC1=CNC=N1)NC(=O)[C@@H](NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C )NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](C)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CO)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CO)NC(=O )[C@H](CCC(N)=O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC( C)C)NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H]( CCCNC(=N)N)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC (=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)CNC(=O)CN)[C@@H](C )O)[C@@H](C)O)C(C)C)[C@@H](C)CC)C(C)C)C(C)C)[C@@H](C)CC)C(C)C)[C@@H](C)CC)[C@@H](C)O)[C@@H](C)O)C(C)C)[C@@H](C)C C)C(C)C)[C@@H](C)CC)C(C)C)[C@@H](C)CC)[C@@H](C)O)C(C)C)[C@@H](C)CC)C(C)C)C(=O)N[C@@H](CCC(N)=O)C(=O)NCC(=O)N[C@@ H](CC1=CC=C(O)C=C1)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCNC (=N)N)C(=O)N[C@@H](CC1=CC=C(O)C=C1)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CC1=CNC =N1)C1=OO1. 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CC[C@H](C)[C@H] (NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)CNC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)CNC(=O)[C@@H](NC(=O)[C@H](C)NC(=O )[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@H](CO)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CO)NC(=O)[C@H ](CC1=CNC=N1)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CCC(=O)O)NC(=O)[ C@H](CCC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H](NC(=O)[C@H](CC(N)=O)NC(=O )[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C) | deepchem.pdf |
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CC[C@H](C)[C@H](NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)CNC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)CNC(=O)[C@@H ](NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@H](CO)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(= O)[C@H](CO)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[ C@H](CCC(=O)O)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H](NC(= O)[C@H](CC(N)=O)NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H | deepchem.pdf |
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CC[C@H](C)[C@H](NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)CNC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@ H](NC(=O)CNC(=O)[C@@H](NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@H](CO)NC(=O)[C@H](CC(C)C)NC(=O)[C@H] (CC1=CC=C(O)C=C1)NC(=O)[C@H](CO)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H] (CC1=CC=CC=C1)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](C)NC(=O)[C@H](CC1=CNC =N1)NC(=O)[C@@H](NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H](NC(=O)[C@H] | deepchem.pdf |
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CC[C@H](C)[C@H](NC(=O)[C@@H](NC(=O)[C@H](CC(N) =O)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CC(C)C)[C@@H](C)CC)C(C)C)C(C)C)C(=O)N[C@@H](CC(N)=O)C(=O)NCC(=O)N1CC C[C@H]1C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)N [C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@H](C=O)CC1C2=OO21. CC[C@H](C)[C@H](NC(=O)[C@@H](NC(=O)[C@H](CC (N)=O)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CC(C)C)[C@@H](C)CC)C(C)C)C(C)C)C(=O)N[C@@H](CC(N)=O)C(=O)NCC(=O)N 1CCC[C@H]1C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC(C)C)C(=O)NCC(= O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@H](C=O)CCC(=O)O. CC[C@H](C)[C@H](NC(=O)[C@@H](NC(=O)[C@H](C C(N)=O)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H](N)CC(C)C)[C@@H](C)CC)C(C)C)C(C)C)C(=O)N[C@@H](CC(N)=O)C(=O)NCC(=O) N1CCC[C@H]1C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC(C)C)C(=O)NCC( =O)N[C@@H](CCCNC(=N)N)C(=O)N[C@H](C=O)CCCNC1=NN1. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O | deepchem.pdf |
. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. OC(O)[C@@]1(O)CC[C@@H](O)[C@H](O)C1. OC(O)[C@]1(O)CC[C@@H](O)[C@H](O)C1. OC (O)[C@]1(O)CC[C@@H](O)[C@H](O)C1. OC(O)[C@]1(O)CC[C@@H](O)[C@H](O)C1. OC(O)[C@]1(O)CC[C@@H](O)[C@H](O)C1. OC(O)[C@] 1(O)CC[C@@H](O)[C@H](O)C1. OC(O)[C@]1(O)CC[C@@H](O)[C@H](O)C1. OC(O)[C@]1(O)CC[C@@H](O)[C@H](O)C1. OC(O)[C@]1(O)CC[ C@@H](O)[C@H](O)C1. OC(O)[C@]1(O)CC[C@@H](O)[C@H](O)C1. OC(O)[C@]1(O)CC[C@@H](O)[C@H](O)C1. OC(O)[C@]1(O)CC[C@@H](O )[C@H](O)C1. OC(O)[C@]1(O)CC[C@@H](O)[C@H](O)C1. OC(O)[C@]1(O)CC[C@@H](O)[C@H](O)C1. OC(O)[C@]1(O)CC[C@@H](O)[C@H]( O)C1. OC(O)[C@]1(O)CC[C@@H](O)[C@H](O)C1. OC(O)[C@]1(O)CC[C@@H](O)[C@H](O)C1. OC(O)[C@]1(O)CC[C@@H](O)[C@H](O)C1. OC (O)[C@]1(O)CC[C@@H](O)[C@H](O)C1. OC(O)[C@]1(O)CC[C@@H](O)[C@H](O)C1. OC(O)[C@]1(O)CC[C@@H](O)[C@H](O)C1. OC(O)[C@] 1(O)CC[C@@H](O)[C@H](O)C1. OC(O)[C@]1(O)CC[C@@H](O)[C@H](O)C1. OCC(O)CO. OCC(O)CO. OCC(O)CO. OCC(O)CO. OCC(O)CO. OCC(O) CO. OCC(O)CO. OCC(O)CO. OCC(O)CO. OCC(O)CO. OCC(O)CO. OCC(O)CO. O[C@@H]1C[C@@]23O[C@@]2(C[C@H]1O)C3(O)O failed sanitiza tion [15:30:34] Explicit valence for atom # 3443 O, 3, is greater than permitted Mol CC1C[C@H]2C(C)C([C@@H]3C[C@H](C)C(O)O3)CC[C@@]23CCCNC3CCCC(C)[C@@H]2O[C@@H](CC[C@@H]1O)C[C@H]2C. CC1C[C@H]2C( C)C([C@@H]3C[C@H](C)C(O)O3)CC[C@@]23CCCNC3CCCC(C)[C@@H]2O[C@@H](CC[C@@H]1O)C[C@H]2C. CC1C[C@H]2C(C)C([C@@H]3C[C@H ](C)C(O)O3)CC[C@@]23CCCNC3CCCC(C)[C@@H]2O[C@@H](CC[C@@H]1O)C[C@H]2C. CC1C[C@H]2C(C)C([C@@H]3C[C@H](C)C(O)O3)CC[C@ @]23CCCNC3CCCC(C)[C@@H]2O[C@@H](CC[C@@H]1O)C[C@H]2C. CC1C[C@H]2C(C)C([C@@H]3C[C@H](C)C(O)O3)CC[C@@]23CCCN[C@@H]3C CCC(C)[C@@H]2O[C@@H](CC[C@@H]1O)C[C@H]2C. CC[C@H](C)[C@H](NC(=O)[C@H](CCC(N)=O)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CO)NC (=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@H](CCC(N)=O)NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@H](CCCN1NC1=N)NC(=O)[C @@H](NC(=O)[C@H](CO)NC(=O)[C@H](CO)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@ H](CC(=O)O)NC(=O)[C@@H]1CCCN1C(=O)[C@@H](NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(=O)[ C@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CC( =O)O)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](CC(N)=O)NC(=O)CNC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CCC(=O)O)NC(=O )[C@H](CC(N)=O)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@H](CCSC)NC(=O)[C@H]( CC(C)C)NC(=O)[C@H](CO)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC (=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CCC(=O)O)NC(=O)[C@H](CC1=CC=C(O)C=C1) NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](NC(=O)[C@H](CCC(=O) O)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)[C@H](CO)NC(=O)[C@H](CO)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](C)NC(=O)[C@H](C CCCN)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C @H](CC1=CC=CC=C1)NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@@H ]1CCCN1C(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCCCN)NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)CNC(=O)[C@@H ]1CCCN1C(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CCSC)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CO)NC(=O)[C@H](CCCNC(=N)N)NC(=O) [C@H](CC(N)=O)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CO)NC(=O)[C@H](CCCCN)N C(=O)[C@H](CC(C)C)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CCSC)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](C)N C(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CO)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC(=O )O)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](N)CC(=O)O)[C@@H](C)O)[C@@H](C)O)C(C)C)[C@@H](C)O)[C@@H]( C)O)[C@@H](C)CC)C(C)C)[C@@H](C)O)C(C)C)C(C)C)[C@@H](C)CC)[C@@H](C)O)[C@@H](C)O)[C@@H](C)CC)[C@@H](C)O)[C@@H](C)C C)[C@@H](C)O)[C@@H](C)O)C(C)C)C(C)C)C(=O)N[C@@H](C)C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@@H](CC1=CNC=N1)C (=O)N[C@@H](CC(=O)O)C(=O)NCC(=O)N[C@@H](CO)C(=O)N[C@H](C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CC1=CC=CC=C1)C(=O)N[C@H](C (=O)N1CCC[C@H]1C(=O)N[C@@H](C)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CO) C(=O)N[C@@H](CC1=CC=CC=C1)C(=O)N[C@@H](CCSC)C(=O)N[C@H]1CSSC[C@@H](C(=O)N[C@@H](C)C(=O)N[C@H](C(=O)N[C@@H](CCCCN )C(=O)N[C@@H](CC2=CC=CC=C2)C(=O)NCC(=O)N[C@@H](CO)C(=O)N[C@@H](CC2=CNC3=C2C=CC=C3)C(=O)N[C@H](C(=O)N[C@@H](CC2=C C=C(O)C=C2)C(=O)N[C@@H](CO)C(=O)NCC(=O)N[C@@H](CC2=CC=CC=C2)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@H](C(=O)N[C@@H](CC(=O )O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCCN)C(=O)N[C@H](C(=O)N[C@@H](CC(=O)O)C(=O)N[C@H](C(=O)N[C@@H](CC(=O)O)C(=O )N[C@@H](CCC(N)=O)C(=O)N[C@H](C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CO)C(=O)N[C@@H](CO)C(=O)N[C@ @H](CC2=CC=C(O)C=C2)C(=O)N[C@@H](CC2=CC=C(O)C=C2)C(=O)N[C@@H](C)C(=O)N[C@@H](CO)C(=O)N[C@@H](CO)C(=O)N[C@@H](CCC CN)C(=O)N[C@@H](CC2=CC=C(O)C=C2)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@H](C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CO)C(=O)N[C@@ H](C)C(=O)N[C@H](C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@H](C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@H](C(= O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CC2=CNC=N2)C(=O)N[C@@H](CC2=CC=C(O)C=C2)C(=O)N[C@@H](CO)C(=O)N[C@H]2CSSC[C@@H](C (=O)N3CCC[C@H]3C(=O)N[C@@H](CCC(=O)O)C(=O)N3CCC[C@H]3C(=O)N[C@@H](CC3=CC=C(O)C=C3)C(=O)N[C@H](C(=O)N[C@@H](CC(=O )O)C(=O)N[C@H](C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@H](C(=O)N[C@H](C(=O)N[C@@H](CCCCN)C(=O)N[C@@H]( | deepchem.pdf |
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CC[C@H](C) [C@H](NC(=O)[C@H](CCC(N)=O)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CO)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@H](CCC(N)=O) NC(=O)[C@@H](NC(=O)[C@@H]1CCCN1C(=O)[C@H](CCCNC(=N)N)NC(=O)[C@@H](NC(=O)[C@H](CO)NC(=O)[C@H](CO)NC(=O)[C@H](CC1= CC=C(O)C=C1)NC(=O)[C@H](C)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H]1CCCN1C(=O)[C@@H](NC(=O)[C@H | deepchem.pdf |
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O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O failed sanitization [15:30:40] Explicit valence for atom # 1686 O, 3, is greater than permitted Mol CC[C@H](C)[C@H](NC(=O)[C@H](CCC(N)=O)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CO)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[ | deepchem.pdf |
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NCCC1CNC2CCC(O)CC12. NCCC1CNC2CCC(O)CC12. NCCC1CNC2CCC(O) CC12. NCCC1CNC2CCC(O)CC12. NCCC1CNC2CCC(O)CC12. NCCC1CNC2CCC(O)CC12. NCCC1CNC2CCC(O)CC12. NCCC1CNC2CCC(O)CC12. NCCC1CN C2CCC(O)CC12. NCCC1CNC2CCC(O)CC12. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O | deepchem.pdf |
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O[PH](O)(O)O failed sanitization [15:30:46] Explicit valence for atom # 3393 O, 3, is greater than permitted Mol CCC1[C@@H]2CC(C)C[C@@]1(N)C1CCC(O)NC1C2. CC[C@H](C)[C@H](NC(=O)CNC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC(N)=O)NC(=O )[C@H](CC(=O)O)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCSC)NC(=O)[C@H](CC1=CNC2=C1C=CC=C2)NC(=O)[C@H](CC(=O)O)NC(=O)[C@ @H](NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@@H](NC(=O)[C@H](C) NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC(C)C)NC(=O)CNC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC(N)=O)NC(=O)[ C@H](C)NC(=O)[C@H](CC1=CNC=N1)NC(=O)[C@@H]1CCCN1C(=O)[C@@H](NC(=O)[C@H](CO)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCCCN) NC(=O)[C@@H](NC(=O)CNC(=O)[C@H](CO)NC(=O)[C@H](CCSC)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCC (=O)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CCCCN)NC(=O)[C@H](CO)NC(=O)[C@H](CCC(=O)O)N C(=O)[C@H](CO)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CO)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)CNC(=O)[C@@H]1 CCCN1C(=O)[C@H](C)NC(=O)CNC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC1=CC=CC 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O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O. O failed sa nitization Let's now train a simple random forest model on this dataset. seed = 42 # Set a random seed to get stable results sklearn_model = Random Forest Regressor ( n_estimators = 100, max_features = 'sqrt' ) sklearn_model. random_state = seed model = dc. models. Sklearn Model ( sklearn_model ) model. fit ( train_dataset ) Let's see what our accuracies looks like! metric = dc. metrics. Metric ( dc. metrics. pearson_r2_score ) evaluator = Evaluator ( model, train_dataset, []) train_r2score = evaluator. compute_model_performance ([ metric ]) print ( "RF Train set R^2 %f " % ( train_r2score [ "pearson_r2_score" ])) evaluator = Evaluator ( model, test_dataset, []) test_r2score = evaluator. compute_model_performance ([ metric ]) print ( "RF Test set R^2 %f " % ( test_r2score [ "pearson_r2_score" ])) RF Train set R^2 0. 536155 RF Test set R^2 0. 000014 Ok, it looks like we have lower accuracy than the ligand-only dataset. Nonetheless, it's probably still useful to have a protein-ligand model since it's likely to learn different features than the the pure ligand-only model. Further reading So far we have used Deep Chem's docking module with the Auto Dock Vina backend to generate docking scores for the PDBbind dataset. We trained a simple machine learning model to directly predict binding affinities, based on featurizing the protein-ligand complexes. We might want to try more sophisticated docking protocols, like the deep learning framework gnina. You can read more about using convolutional neural nets for protein-ligand scoring here. And here is a review of machine learning-based scoring functions. Congratulations! Time to join the Community! Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with Deep Chem, we encourage you to finish the rest of the tutorials in this series. You can also help the Deep Chem community in the following ways: | deepchem.pdf |
Star Deep Chem on Git Hub This helps build awareness of the Deep Chem project and the tools for open source drug discovery that we're trying to build. Join the Deep Chem Gitter The Deep Chem Gitter hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation! | deepchem.pdf |
Modeling Protein-Ligand Interactions with Atomic Convolutions By Nathan C. Frey | Twitter and Bharath Ramsundar | Twitter This Deep Chem tutorial introduces the Atomic Convolutional Neural Network. We'll see the structure of the Atomic Conv Model and write a simple program to run Atomic Convolutions. ACNN Architecture ACNN's directly exploit the local three-dimensional structure of molecules to hierarchically learn more complex chemical features by optimizing both the model and featurization simultaneously in an end-to-end fashion. The atom type convolution makes use of a neighbor-listed distance matrix to extract features encoding local chemical environments from an input representation (Cartesian atomic coordinates) that does not necessarily contain spatial locality. The following methods are used to build the ACNN architecture: Distance Matrix The distance matrix is constructed from the Cartesian atomic coordinates. It calculates distances from the distance tensor. The distance matrix construction accepts as input a coordinate matrix. This matrix is “neighbor listed” into a matrix. R = tf. reduce_sum ( tf. multiply ( D, D ), 3 ) # D: Distance Tensor R = tf. sqrt ( R ) # R: Distance Matrix return R Atom type convolution The output of the atom type convolution is constructed from the distance matrix and atomic number matrix. The matrix is fed into a (1x1) filter with stride 1 and depth of, where is the number of unique atomic numbers (atom types) present in the molecular system. The atom type convolution kernel is a step function that operates on the neighbor distance matrix. Radial Pooling layer Radial Pooling is basically a dimensionality reduction process that down-samples the output of the atom type convolutions. The reduction process prevents overfitting by providing an abstracted form of representation through feature binning, as well as reducing the number of parameters learned. Mathematically, radial pooling layers pool over tensor slices (receptive fields) of size (1x x1) with stride 1 and a depth of, where is the number of desired radial filters and is the maximum number of neighbors. Atomistic fully connected network Atomic Convolution layers are stacked by feeding the flattened ( | deepchem.pdf |
, ) output of the radial pooling layer into the atom type convolution operation. Finally, we feed the tensor row-wise (per-atom) into a fully-connected network. The same fully connected weights and biases are used for each atom in a given molecule. Now that we have seen the structural overview of ACNNs, we'll try to get deeper into the model and see how we can train it and what we expect as the output. For the training, we will use the publicly available PDBbind dataset. In this example, every row reflects a protein-ligand complex and the target is the binding affinity ( ) of the ligand to the protein in the complex. Colab This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link. O p e n i n C o l a b O p e n i n C o l a b Setup To run Deep Chem within Colab, you'll need to run the following cell of installation commands. This will take about 5 minutes to run to completion and install your environment. ! pip install -q condacolab import condacolab condacolab. install () ! /usr/local/bin/conda info -e ! /usr/local/bin/conda install -c conda-forge pycosat mdtraj pdbfixer openmm -y -q # needed for Atomic Convs ! pip install --pre deepchem import deepchem deepchem. __version__ import deepchem as dc import os import numpy as np import tensorflow as tf import matplotlib. pyplot as plt from rdkit import Chem from deepchem. molnet import load_pdbbind from deepchem. models import Atomic Conv Model from deepchem. feat import Atomic Conv Featurizer Getting protein-ligand data If you worked through Tutorial 13 on modeling protein-ligand interactions, you'll already be familiar with how to obtain a set of data from PDBbind for training our model. Since we explored molecular complexes in detail in the previous tutorial ), this time we'll simply initialize an Atomic Conv Featurizer and load the PDBbind dataset directly using Mol Net. f1_num_atoms = 100 # maximum number of atoms to consider in the ligand f2_num_atoms = 1000 # maximum number of atoms to consider in the protein max_num_neighbors = 12 # maximum number of spatial neighbors for an atom acf = Atomic Conv Featurizer ( frag1_num_atoms = f1_num_atoms, frag2_num_atoms = f2_num_atoms, complex_num_atoms = f1_num_atoms + f2_num_atoms, max_num_neighbors = max_num_neighbors, neighbor_cutoff = 4 ) load_pdbbind allows us to specify if we want to use the entire protein or only the binding pocket ( pocket=True ) for featurization. Using only the pocket saves memory and speeds up the featurization. We can also use the "core" dataset | deepchem.pdf |
of ~200 high-quality complexes for rapidly testing our model, or the larger "refined" set of nearly 5000 complexes for more datapoints and more robust training/validation. On Colab, it takes only a minute to featurize the core PDBbind set! This is pretty incredible, and it means you can quickly experiment with different featurizations and model architectures. %%time tasks, datasets, transformers = load_pdbbind ( featurizer = acf, save_dir = '. ', data_dir = '. ', pocket = True, reload = False, set_name = 'core' ) /usr/local/lib/python3. 7/dist-packages/numpy/core/_asarray. py:83: Visible Deprecation Warning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray return array(a, dtype, copy=False, order=order) CPU times: user 43. 2 s, sys: 18. 6 s, total: 1min 1s Wall time: 1min 10s Unfortunately, if you try to use the "refined" dataset, there are some complexes that cannot be featurized. To resolve this issue, rather than increasing complex_num_atoms, simply omit the lines of the dataset that have an x value of None class My Transformer ( dc. trans. Transformer ): def transform_array ( x, y, w, ids ): kept_rows = x != None return x [ kept_rows ], y [ kept_rows ], w [ kept_rows ], ids [ kept_rows ], datasets = [ d. transform ( My Transformer ) for d in datasets ] datasets (<Disk Dataset X. shape: (154, 9), y. shape: (154,), w. shape: (154,), ids: ['1mq6' '3pe2' '2wtv' ... '3f3c' '4gqq' '2x00'], task_names: [0]>, <Disk Dataset X. shape: (19, 9), y. shape: (19,), w. shape: (19,), ids: ['3ivg' '4de1' '4tmn' ... '2vw5' '1w3l' '2 zjw'], task_names: [0]>, <Disk Dataset X. shape: (20, 9), y. shape: (20,), w. shape: (20,), ids: ['1kel' '2w66' '2xnb' ... '2qbp' '3lka' '1 qi0'], task_names: [0]>) train, val, test = datasets Training the model Now that we've got our dataset, let's go ahead and initialize an Atomic Conv Model to train. Keep the input parameters the same as those used in Atomic Conv Featurizer, or else we'll get errors. layer_sizes controls the number of layers and the size of each dense layer in the network. We choose these hyperparameters to be the same as those used in the original paper. acm = Atomic Conv Model ( n_tasks = 1, frag1_num_atoms = f1_num_atoms, frag2_num_atoms = f2_num_atoms, complex_num_atoms = f1_num_atoms + f2_num_atoms, max_num_neighbors = max_num_neighbors, batch_size = 12, layer_sizes = [ 32, 32, 16 ], learning_rate = 0. 003, ) losses, val_losses = [], [] %%time max_epochs = 50 metric = dc. metrics. Metric ( dc. metrics. score_function. rms_score ) step_cutoff = len ( train ) // 12 def val_cb ( model, step ): if step % step_cutoff != 0 : return val_losses. append ( model. evaluate ( val, metrics = [ metric ])[ 'rms_score' ] ** 2 ) # L2 Loss losses. append ( model. evaluate ( train, metrics = [ metric ])[ 'rms_score' ] ** 2 ) # L2 Loss acm. fit ( train, nb_epoch = max_epochs, max_checkpoints_to_keep = 1, callbacks = [ val_cb ]) CPU times: user 2min 41s, sys: 11. 4 s, total: 2min 53s Wall time: 2min 47s The loss curves are not exactly smooth, which is unsurprising because we are using 154 training and 19 validation | deepchem.pdf |
datapoints. Increasing the dataset size may help with this, but will also require greater computational resources. f, ax = plt. subplots () ax. scatter ( range ( len ( losses )), losses, label = 'train loss' ) ax. scatter ( range ( len ( val_losses )), val_losses, label = 'val loss' ) plt. legend ( loc = 'upper right' ); The ACNN paper showed a Pearson score of 0. 912 and 0. 448 for a random 80/20 split of the PDBbind core train/test sets. Here, we've used an 80/10/10 training/validation/test split and achieved similar performance for the training set (0. 943). We can see from the performance on the training, validation, and test sets (and from the results in the paper) that the ACNN can learn chemical interactions from small training datasets, but struggles to generalize. Still, it is pretty amazing that we can train an Atomic Conv Model with only a few lines of code and start predicting binding affinities! From here, you can experiment with different hyperparameters, more challenging splits, and the "refined" set of PDBbind to see if you can reduce overfitting and come up with a more robust model. score = dc. metrics. Metric ( dc. metrics. score_function. pearson_r2_score ) for tvt, ds in zip ([ 'train', 'val', 'test' ], datasets ): print ( tvt, acm. evaluate ( ds, metrics = [ score ])) train {'pearson_r2_score': 0. 9311347622675604 val {'pearson_r2_score': 0. 5162870575992874} test {'pearson_r2_score': 0. 4756633065901693} Further reading We have explored the ACNN architecture and used the PDBbind dataset to train an ACNN to predict protein-ligand binding energies. For more information, read the original paper that introduced ACNNs: Gomes, Joseph, et al. "Atomic convolutional networks for predicting protein-ligand binding affinity. " ar Xiv preprint ar Xiv:1703. 10603 (2017). There are many other methods and papers on predicting binding affinities. Here are a few interesting ones to check out: predictions using only ligands or proteins, molecular docking with deep learning, and Atom Net. Congratulations! Time to join the Community! Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with Deep Chem, we encourage you to finish the rest of the tutorials in this series. You can also help the Deep Chem community in the following ways: Star Deep Chem on Git Hub This helps build awareness of the Deep Chem project and the tools for open source drug discovery that we're trying to build. Join the Deep Chem Gitter The Deep Chem Gitter hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation! | deepchem.pdf |
Conditional Generative Adversarial Network A Generative Adversarial Network (GAN) is a type of generative model. It consists of two parts called the "generator" and the "discriminator". The generator takes random values as input and transforms them into an output that (hopefully) resembles the training data. The discriminator takes a set of samples as input and tries to distinguish the real training samples from the ones created by the generator. Both of them are trained together. The discriminator tries to get better and better at telling real from false data, while the generator tries to get better and better at fooling the discriminator. A Conditional GAN (CGAN) allows additional inputs to the generator and discriminator that their output is conditioned on. For example, this might be a class label, and the GAN tries to learn how the data distribution varies between classes. Colab This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link. O p e n i n C o l a b O p e n i n C o l a b Setup To run Deep Chem within Colab, you'll need to run the following cell of installation commands. ! pip install --pre deepchem import deepchem deepchem. __version__ For this example, we will create a data distribution consisting of a set of ellipses in 2D, each with a random position, shape, and orientation. Each class corresponds to a different ellipse. Let's randomly generate the ellipses. For each one we select a random center position, X and Y size, and rotation angle. We then create a transformation matrix that maps the unit circle to the ellipse. import deepchem as dc import numpy as np import tensorflow as tf n_classes = 4 class_centers = np. random. uniform (-4, 4, ( n_classes, 2 )) class_transforms = [] for i in range ( n_classes ): xscale = np. random. uniform ( 0. 5, 2 ) yscale = np. random. uniform ( 0. 5, 2 ) angle = np. random. uniform ( 0, np. pi ) m = [[ xscale * np. cos ( angle ), -yscale * np. sin ( angle )], [ xscale * np. sin ( angle ), yscale * np. cos ( angle )]] class_transforms. append ( m ) class_transforms = np. array ( class_transforms ) This function generates random data from the distribution. For each point it chooses a random class, then a random position in that class' ellipse. def generate_data ( n_points ): classes = np. random. randint ( n_classes, size = n_points ) r = np. random. random ( n_points ) angle = 2 * np. pi * np. random. random ( n_points ) points = ( r * np. array ([ np. cos ( angle ), np. sin ( angle )])). T points = np. einsum ( 'ijk,ik->ij', class_transforms [ classes ], points ) points += class_centers [ classes ] return classes, points Let's plot a bunch of random points drawn from this distribution to see what it looks like. Points are colored based on their class label. % matplotlib inline import matplotlib. pyplot as plot classes, points = generate_data ( 1000 ) plot. scatter ( x = points [:, 0 ], y = points [:, 1 ], c = classes ) <matplotlib. collections. Path Collection at 0x1584692d0> | deepchem.pdf |
Now let's create the model for our CGAN. Deep Chem's GAN class makes this very easy. We just subclass it and implement a few methods. The two most important are: create_generator() constructs a model implementing the generator. The model takes as input a batch of random noise plus any condition variables (in our case, the one-hot encoded class of each sample). Its output is a synthetic sample that is supposed to resemble the training data. create_discriminator() constructs a model implementing the discriminator. The model takes as input the samples to evaluate (which might be either real training data or synthetic samples created by the generator) and the condition variables. Its output is a single number for each sample, which will be interpreted as the probability that the sample is real training data. In this case, we use very simple models. They just concatenate the inputs together and pass them through a few dense layers. Notice that the final layer of the discriminator uses a sigmoid activation. This ensures it produces an output between 0 and 1 that can be interpreted as a probability. We also need to implement a few methods that define the shapes of the various inputs. We specify that the random noise provided to the generator should consist of ten numbers for each sample; that each data sample consists of two numbers (the X and Y coordinates of a point in 2D); and that the conditional input consists of n_classes numbers for each sample (the one-hot encoded class index). from tensorflow. keras. layers import Concatenate, Dense, Input class Example GAN ( dc. models. GAN ): def get_noise_input_shape ( self ): return ( 10,) def get_data_input_shapes ( self ): return [( 2,)] def get_conditional_input_shapes ( self ): return [( n_classes,)] def create_generator ( self ): noise_in = Input ( shape = ( 10,)) conditional_in = Input ( shape = ( n_classes,)) gen_in = Concatenate ()([ noise_in, conditional_in ]) gen_dense1 = Dense ( 30, activation = tf. nn. relu )( gen_in ) gen_dense2 = Dense ( 30, activation = tf. nn. relu )( gen_dense1 ) generator_points = Dense ( 2 )( gen_dense2 ) return tf. keras. Model ( inputs = [ noise_in, conditional_in ], outputs = [ generator_points ]) def create_discriminator ( self ): data_in = Input ( shape = ( 2,)) conditional_in = Input ( shape = ( n_classes,)) discrim_in = Concatenate ()([ data_in, conditional_in ]) discrim_dense1 = Dense ( 30, activation = tf. nn. relu )( discrim_in ) discrim_dense2 = Dense ( 30, activation = tf. nn. relu )( discrim_dense1 ) discrim_prob = Dense ( 1, activation = tf. sigmoid )( discrim_dense2 ) return tf. keras. Model ( inputs = [ data_in, conditional_in ], outputs = [ discrim_prob ]) gan = Example GAN ( learning_rate = 1e-4 ) Now to fit the model. We do this by calling fit_gan(). The argument is an iterator that produces batches of training data. More specifically, it needs to produce dicts that map all data inputs and conditional inputs to the values to use for them. In our case we can easily create as much random data as we need, so we define a generator that calls the generate_data() function defined above for each new batch. def iterbatches ( batches ): for i in range ( batches ): | deepchem.pdf |
classes, points = generate_data ( gan. batch_size ) classes = dc. metrics. to_one_hot ( classes, n_classes ) yield { gan. data_inputs [ 0 ]: points, gan. conditional_inputs [ 0 ]: classes } gan. fit_gan ( iterbatches ( 5000 )) Ending global_step 999: generator average loss 0. 87121, discriminator average loss 1. 08472 Ending global_step 1999: generator average loss 0. 968357, discriminator average loss 1. 17393 Ending global_step 2999: generator average loss 0. 710444, discriminator average loss 1. 37858 Ending global_step 3999: generator average loss 0. 699195, discriminator average loss 1. 38131 Ending global_step 4999: generator average loss 0. 694203, discriminator average loss 1. 3871 TIMING: model fitting took 31. 352 s Have the trained model generate some data, and see how well it matches the training distribution we plotted before. classes, points = generate_data ( 1000 ) one_hot_classes = dc. metrics. to_one_hot ( classes, n_classes ) gen_points = gan. predict_gan_generator ( conditional_inputs = [ one_hot_classes ]) plot. scatter ( x = gen_points [:, 0 ], y = gen_points [:, 1 ], c = classes ) <matplotlib. collections. Path Collection at 0x160dedf50> Congratulations! Time to join the Community! Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with Deep Chem, we encourage you to finish the rest of the tutorials in this series. You can also help the Deep Chem community in the following ways: Star Deep Chem on Git Hub This helps build awareness of the Deep Chem project and the tools for open source drug discovery that we're trying to build. Join the Deep Chem Discord The Deep Chem Discord hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation! | deepchem.pdf |
Training a Generative Adversarial Network on MNIST In this tutorial, we will train a Generative Adversarial Network (GAN) on the MNIST dataset. This is a large collection of 28x28 pixel images of handwritten digits. We will try to train a network to produce new images of handwritten digits. Colab This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link. O p e n i n C o l a b O p e n i n C o l a b ! pip install --pre deepchem import deepchem deepchem. __version__ To begin, let's import all the libraries we'll need and load the dataset (which comes bundled with Tensorflow). import deepchem as dc import tensorflow as tf from deepchem. models. optimizers import Exponential Decay from tensorflow. keras. layers import Conv2D, Conv2DTranspose, Dense, Reshape import matplotlib. pyplot as plot import matplotlib. gridspec as gridspec % matplotlib inline mnist = tf. keras. datasets. mnist. load_data ( path = 'mnist. npz' ) images = mnist [ 0 ][ 0 ]. reshape ((-1, 28, 28, 1 )) / 255 dataset = dc. data. Numpy Dataset ( images ) Let's view some of the images to get an idea of what they look like. def plot_digits ( im ): plot. figure ( figsize = ( 3, 3 )) grid = gridspec. Grid Spec ( 4, 4, wspace = 0. 05, hspace = 0. 05 ) for i, g in enumerate ( grid ): ax = plot. subplot ( g ) ax. set_xticks ([]) ax. set_yticks ([]) ax. imshow ( im [ i,:,:, 0 ], cmap = 'gray' ) plot_digits ( images ) Now we can create our GAN. Like in the last tutorial, it consists of two parts: 1. The generator takes random noise as its input and produces output that will hopefully resemble the training data. 2. The discriminator takes a set of samples as input (possibly training data, possibly created by the generator), and tries to determine which are which. This time we will use a different style of GAN called a Wasserstein GAN (or WGAN for short). In many cases, they are found to produce better results than conventional GANs. The main difference between the two is in the discriminator (often called a "critic" in this context). Instead of outputting the probability of a sample being real training data, it tries to learn how to measure the distance between the training distribution and generated distribution. That measure can then be directly used as a loss function for training the generator. We use a very simple model. The generator uses a dense layer to transform the input noise into a 7x7 image with eight channels. That is followed by two convolutional layers that upsample it first to 14x14, and finally to 28x28. The discriminator does roughly the same thing in reverse. Two convolutional layers downsample the image first to 14x14, then to 7x7. A final dense layer produces a single number as output. In the last tutorial we used a sigmoid activation to produce a number between 0 and 1 that could be interpreted as a probability. Since this is a WGAN, we | deepchem.pdf |
instead use a softplus activation. It produces an unbounded positive number that can be interpreted as a distance. class Digit GAN ( dc. models. WGAN ): def get_noise_input_shape ( self ): return ( 10,) def get_data_input_shapes ( self ): return [( 28, 28, 1 )] def create_generator ( self ): return tf. keras. Sequential ([ Dense ( 7 * 7 * 8, activation = tf. nn. relu ), Reshape (( 7, 7, 8 )), Conv2DTranspose ( filters = 16, kernel_size = 5, strides = 2, activation = tf. nn. relu, padding = 'same' ), Conv2DTranspose ( filters = 1, kernel_size = 5, strides = 2, activation = tf. sigmoid, padding = 'same' ) ]) def create_discriminator ( self ): return tf. keras. Sequential ([ Conv2D ( filters = 32, kernel_size = 5, strides = 2, activation = tf. nn. leaky_relu, padding = 'same' ), Conv2D ( filters = 64, kernel_size = 5, strides = 2, activation = tf. nn. leaky_relu, padding = 'same' ), Dense ( 1, activation = tf. math. softplus ) ]) gan = Digit GAN ( learning_rate = Exponential Decay ( 0. 001, 0. 9, 5000 )) Now to train it. As in the last tutorial, we write a generator to produce data. This time the data is coming from a dataset, which we loop over 100 times. One other difference is worth noting. When training a conventional GAN, it is important to keep the generator and discriminator in balance thoughout training. If either one gets too far ahead, it becomes very difficult for the other one to learn. WGANs do not have this problem. In fact, the better the discriminator gets, the cleaner a signal it provides and the easier it becomes for the generator to learn. We therefore specify generator_steps=0. 2 so that it will only take one step of training the generator for every five steps of training the discriminator. This tends to produce faster training and better results. def iterbatches ( epochs ): for i in range ( epochs ): for batch in dataset. iterbatches ( batch_size = gan. batch_size ): yield { gan. data_inputs [ 0 ]: batch [ 0 ]} gan. fit_gan ( iterbatches ( 100 ), generator_steps = 0. 2, checkpoint_interval = 5000 ) Ending global_step 4999: generator average loss 0. 340072, discriminator average loss -0. 0234236 Ending global_step 9999: generator average loss 0. 52308, discriminator average loss -0. 00702729 Ending global_step 14999: generator average loss 0. 572661, discriminator average loss -0. 00635684 Ending global_step 19999: generator average loss 0. 560454, discriminator average loss -0. 00534357 Ending global_step 24999: generator average loss 0. 556055, discriminator average loss -0. 00620613 Ending global_step 29999: generator average loss 0. 541958, discriminator average loss -0. 00734233 Ending global_step 34999: generator average loss 0. 540904, discriminator average loss -0. 00736641 Ending global_step 39999: generator average loss 0. 524298, discriminator average loss -0. 00650514 Ending global_step 44999: generator average loss 0. 503931, discriminator average loss -0. 00563732 Ending global_step 49999: generator average loss 0. 528964, discriminator average loss -0. 00590612 Ending global_step 54999: generator average loss 0. 510892, discriminator average loss -0. 00562366 Ending global_step 59999: generator average loss 0. 494756, discriminator average loss -0. 00533636 TIMING: model fitting took 4197. 860 s Let's generate some data and see how the results look. plot_digits ( gan. predict_gan_generator ( batch_size = 16 )) Not too bad. Many of the generated images look plausibly like handwritten digits. A larger model trained for a longer time can do much better, of course. | deepchem.pdf |
Congratulations! Time to join the Community! Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with Deep Chem, we encourage you to finish the rest of the tutorials in this series. You can also help the Deep Chem community in the following ways: Star Deep Chem on Git Hub This helps build awareness of the Deep Chem project and the tools for open source drug discovery that we're trying to build. Join the Deep Chem Gitter The Deep Chem Gitter hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation! | deepchem.pdf |
Advanced model training using hyperopt In the Advanced Model Training tutorial we have already taken a look into hyperparameter optimasation using Grid Hyperparam Opt in the deepchem pacakge. In this tutorial, we will take a look into another hyperparameter tuning library called hyperopt. Colab This tutorial and the rest in this sequence can be done in Google colab. If you'd like to open this notebook in colab, you can use the following link. O p e n i n C o l a b O p e n i n C o l a b Setup To run Deep Chem and Hyperopt within Colab, you'll need to run the following installation commands. You can of course run this tutorial locally if you prefer. In that case, don't run these cells since they will download and install Deep Chem and Hyperopt in your local machine again. ! pip install deepchem ! pip install hyperopt Collecting deepchem Downloading deepchem-2. 6. 1-py3-none-any. whl (608 k B) |▌ | 10 k B 31. 6 MB/s eta 0:00:01 |█ | 20 k B 27. 2 MB/s eta 0:00:01 |█▋ | 30 k B 11. 2 MB/s eta 0:00:01 |██▏ | 40 k B 8. 9 MB/s eta 0:00:01 |██▊ | 51 k B 5. 3 MB/s eta 0:00:01 |███▎ | 61 k B 5. 4 MB/s eta 0:00:01 |███▊ | 71 k B 5. 4 MB/s eta 0:00:01 |████▎ | 81 k B 6. 1 MB/s eta 0:00:01 |████▉ | 92 k B 6. 2 MB/s eta 0:00:01 |█████▍ | 102 k B 5. 2 MB/s eta 0:00:01 |██████ | 112 k B 5. 2 MB/s eta 0:00:01 |██████▌ | 122 k B 5. 2 MB/s eta 0:00:01 |███████ | 133 k B 5. 2 MB/s eta 0:00:01 |███████▌ | 143 k B 5. 2 MB/s eta 0:00:01 |████████ | 153 k B 5. 2 MB/s eta 0:00:01 |████████▋ | 163 k B 5. 2 MB/s eta 0:00:01 |█████████▏ | 174 k B 5. 2 MB/s eta 0:00:01 |█████████▊ | 184 k B 5. 2 MB/s eta 0:00:01 |██████████▎ | 194 k B 5. 2 MB/s eta 0:00:01 |██████████▊ | 204 k B 5. 2 MB/s eta 0:00:01 |███████████▎ | 215 k B 5. 2 MB/s eta 0:00:01 |███████████▉ | 225 k B 5. 2 MB/s eta 0:00:01 |████████████▍ | 235 k B 5. 2 MB/s eta 0:00:01 |█████████████ | 245 k B 5. 2 MB/s eta 0:00:01 |█████████████▌ | 256 k B 5. 2 MB/s eta 0:00:01 |██████████████ | 266 k B 5. 2 MB/s eta 0:00:01 |██████████████▌ | 276 k B 5. 2 MB/s eta 0:00:01 |███████████████ | 286 k B 5. 2 MB/s eta 0:00:01 |███████████████▋ | 296 k B 5. 2 MB/s eta 0:00:01 |████████████████▏ | 307 k B 5. 2 MB/s eta 0:00:01 |████████████████▊ | 317 k B 5. 2 MB/s eta 0:00:01 |█████████████████▎ | 327 k B 5. 2 MB/s eta 0:00:01 |█████████████████▊ | 337 k B 5. 2 MB/s eta 0:00:01 |██████████████████▎ | 348 k B 5. 2 MB/s eta 0:00:01 |██████████████████▉ | 358 k B 5. 2 MB/s eta 0:00:01 |███████████████████▍ | 368 k B 5. 2 MB/s eta 0:00:01 |████████████████████ | 378 k B 5. 2 MB/s eta 0:00:01 |████████████████████▌ | 389 k B 5. 2 MB/s eta 0:00:01 |█████████████████████ | 399 k B 5. 2 MB/s eta 0:00:01 |█████████████████████▌ | 409 k B 5. 2 MB/s eta 0:00:01 |██████████████████████ | 419 k B 5. 2 MB/s eta 0:00:01 |██████████████████████▋ | 430 k B 5. 2 MB/s eta 0:00:01 |███████████████████████▏ | 440 k B 5. 2 MB/s eta 0:00:01 |███████████████████████▊ | 450 k B 5. 2 MB/s eta 0:00:01 |████████████████████████▎ | 460 k B 5. 2 MB/s eta 0:00:01 |████████████████████████▉ | 471 k B 5. 2 MB/s eta 0:00:01 |█████████████████████████▎ | 481 k B 5. 2 MB/s eta 0:00:01 |█████████████████████████▉ | 491 k B 5. 2 MB/s eta 0:00:01 |██████████████████████████▍ | 501 k B 5. 2 MB/s eta 0:00:01 |███████████████████████████ | 512 k B 5. 2 MB/s eta 0:00:01 | deepchem.pdf |
|███████████████████████████▌ | 522 k B 5. 2 MB/s eta 0:00:01 |████████████████████████████ | 532 k B 5. 2 MB/s eta 0:00:01 |████████████████████████████▌ | 542 k B 5. 2 MB/s eta 0:00:01 |█████████████████████████████ | 552 k B 5. 2 MB/s eta 0:00:01 |█████████████████████████████▋ | 563 k B 5. 2 MB/s eta 0:00:01 |██████████████████████████████▏ | 573 k B 5. 2 MB/s eta 0:00:01 |██████████████████████████████▊ | 583 k B 5. 2 MB/s eta 0:00:01 |███████████████████████████████▎| 593 k B 5. 2 MB/s eta 0:00:01 |███████████████████████████████▉| 604 k B 5. 2 MB/s eta 0:00:01 |████████████████████████████████| 608 k B 5. 2 MB/s Requirement already satisfied: scipy in /usr/local/lib/python3. 7/dist-packages (from deepchem) (1. 4. 1) Collecting numpy>=1. 21 Downloading numpy-1. 21. 5-cp37-cp37m-manylinux_2_12_x86_64. manylinux2010_x86_64. whl (15. 7 MB) |████████████████████████████████| 15. 7 MB 25. 3 MB/s Requirement already satisfied: scikit-learn in /usr/local/lib/python3. 7/dist-packages (from deepchem) (1. 0. 2) Requirement already satisfied: pandas in /usr/local/lib/python3. 7/dist-packages (from deepchem) (1. 3. 5) Collecting rdkit-pypi Downloading rdkit_pypi-2021. 9. 4-cp37-cp37m-manylinux_2_17_x86_64. manylinux2014_x86_64. whl (20. 6 MB) |████████████████████████████████| 20. 6 MB 1. 4 MB/s Requirement already satisfied: joblib in /usr/local/lib/python3. 7/dist-packages (from deepchem) (1. 1. 0) Requirement already satisfied: pytz>=2017. 3 in /usr/local/lib/python3. 7/dist-packages (from pandas->deepchem) (2 018. 9) Requirement already satisfied: python-dateutil>=2. 7. 3 in /usr/local/lib/python3. 7/dist-packages (from pandas->de epchem) (2. 8. 2) Requirement already satisfied: six>=1. 5 in /usr/local/lib/python3. 7/dist-packages (from python-dateutil>=2. 7. 3-> pandas->deepchem) (1. 15. 0) Requirement already satisfied: Pillow in /usr/local/lib/python3. 7/dist-packages (from rdkit-pypi->deepchem) (7. 1. 2) Requirement already satisfied: threadpoolctl>=2. 0. 0 in /usr/local/lib/python3. 7/dist-packages (from scikit-learn->deepchem) (3. 1. 0) Installing collected packages: numpy, rdkit-pypi, deepchem Attempting uninstall: numpy Found existing installation: numpy 1. 19. 5 Uninstalling numpy-1. 19. 5: Successfully uninstalled numpy-1. 19. 5 ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. yellowbrick 1. 3. post1 requires numpy<1. 20,>=1. 16. 0, but you have numpy 1. 21. 5 which is incompatible. datascience 0. 10. 6 requires folium==0. 2. 1, but you have folium 0. 8. 3 which is incompatible. albumentations 0. 1. 12 requires imgaug<0. 2. 7,>=0. 2. 5, but you have imgaug 0. 2. 9 which is incompatible. Successfully installed deepchem-2. 6. 1 numpy-1. 21. 5 rdkit-pypi-2021. 9. 4 Requirement already satisfied: hyperopt in /usr/local/lib/python3. 7/dist-packages (0. 1. 2) Requirement already satisfied: networkx in /usr/local/lib/python3. 7/dist-packages (from hyperopt) (2. 6. 3) Requirement already satisfied: future in /usr/local/lib/python3. 7/dist-packages (from hyperopt) (0. 16. 0) Requirement already satisfied: pymongo in /usr/local/lib/python3. 7/dist-packages (from hyperopt) (4. 0. 1) Requirement already satisfied: scipy in /usr/local/lib/python3. 7/dist-packages (from hyperopt) (1. 4. 1) Requirement already satisfied: numpy in /usr/local/lib/python3. 7/dist-packages (from hyperopt) (1. 21. 5) Requirement already satisfied: tqdm in /usr/local/lib/python3. 7/dist-packages (from hyperopt) (4. 62. 3) Requirement already satisfied: six in /usr/local/lib/python3. 7/dist-packages (from hyperopt) (1. 15. 0) Hyperparameter Optimization via hyperopt Let's start by loading the HIV dataset. It classifies over 40,000 molecules based on whether they inhibit HIV replication. import deepchem as dc tasks, datasets, transformers = dc. molnet. load_hiv ( featurizer = 'ECFP', split = 'scaffold' ) train_dataset, valid_dataset, test_dataset = datasets 'split' is deprecated. Use 'splitter' instead. Now, lets import the hyperopt library, which we will be using to fund the best parameters from hyperopt import hp, fmin, tpe, Trials Then we have to declare a dictionary with all the hyperparameters and their range that you will be tuning them in. This dictionary will serve as the search space for the hyperopt. Some basic ways of declaring the ranges in the dictionary are: hp. choice('label',[ choices ]) : this is used to specify a list of choices hp. uniform('label' ,low= low_value ,high= high_value ) : this is used to specify a uniform distibution between the low and high values. The values between them can be any real number, not necessaarily an integer. Here, we are going to use a multitaskclassifier to classify the HIV dataset and hence the appropriate search space is as follows. search_space = { 'layer_sizes' : hp. choice ( 'layer_sizes',[[ 500 ], [ 1000 ], [ 2000 ],[ 1000, 1000 ]]), 'dropouts' : hp. uniform ( 'dropout', low = 0. 2, high = 0. 5 ), 'learning_rate' : hp. uniform ( 'learning_rate', high = 0. 001, low = 0. 0001 ) | deepchem.pdf |
} We should then declare a function to be minimized by the hyperopt. So, here we should use the function to minimize our multitaskclassifier model. Additionally, we are using a validation callback to validate the classifier for every 1000 steps, then we are passing the best score as the return. The metric used here is 'roc_auc_score', which needs to be maximized. To maximize a non-negative value is equivalent to minimize its opposite number, hence we are returning the negative of the validation score. import tempfile #tempfile is used to save the best checkpoint later in the program. metric = dc. metrics. Metric ( dc. metrics. roc_auc_score ) def fm ( args ): save_dir = tempfile. mkdtemp () model = dc. models. Multitask Classifier ( n_tasks = len ( tasks ), n_features = 1024, layer_sizes = args [ 'layer_sizes' ], dropouts #validation callback that saves the best checkpoint, i. e the one with the maximum score. validation = dc. models. Validation Callback ( valid_dataset, 1000, [ metric ], save_dir = save_dir, transformers = transformers model. fit ( train_dataset, nb_epoch = 25, callbacks = validation ) #restoring the best checkpoint and passing the negative of its validation score to be minimized. model. restore ( model_dir = save_dir ) valid_score = model. evaluate ( valid_dataset, [ metric ], transformers ) return -1 * valid_score [ 'roc_auc_score' ] Here, we are calling the fmin function of the hyperopt, where we pass on the function to be minimized, the algorithm to be followed, max number of evals and a trials object. The Trials object is used to keep All hyperparameters, loss, and other information, this means you can access them after running optimization. Also, trials can help you to save important information and later load and then resume the optimization process. Moreover, for the algorithm there are three choice which can be used without any additional configuration. they are :-Random Search - rand. suggest TPE (Tree Parzen Estimators) - tpe. suggest Adaptive TPE - atpe. suggest trials = Trials () best = fmin ( fm, space = search_space, algo = tpe. suggest, max_evals = 15, trials = trials ) 0%| | 0/15 [00:00<?, ?it/s, best loss: ?]Step 1000 validation: roc_auc_score=0. 777648 Step 2000 validation: roc_auc_score=0. 755485 Step 3000 validation: roc_auc_score=0. 739519 Step 4000 validation: roc_auc_score=0. 764756 Step 5000 validation: roc_auc_score=0. 757006 Step 6000 validation: roc_auc_score=0. 752609 Step 7000 validation: roc_auc_score=0. 763002 Step 8000 validation: roc_auc_score=0. 749202 7%|▋ | 1/15 [05:37<1:18:46, 337. 58s/it, best loss: -0. 7776476459925534]Step 1000 validation: roc_auc_s core=0. 750455 Step 2000 validation: roc_auc_score=0. 783594 Step 3000 validation: roc_auc_score=0. 775872 Step 4000 validation: roc_auc_score=0. 768825 Step 5000 validation: roc_auc_score=0. 769555 Step 6000 validation: roc_auc_score=0. 765324 Step 7000 validation: roc_auc_score=0. 771146 Step 8000 validation: roc_auc_score=0. 760138 13%|█▎ | 2/15 [07:05<41:16, 190. 51s/it, best loss: -0. 7835939030962179] Step 1000 validation: roc_auc_s core=0. 744178 Step 2000 validation: roc_auc_score=0. 765406 Step 3000 validation: roc_auc_score=0. 76532 Step 4000 validation: roc_auc_score=0. 769255 Step 5000 validation: roc_auc_score=0. 77029 Step 6000 validation: roc_auc_score=0. 768024 Step 7000 validation: roc_auc_score=0. 764157 Step 8000 validation: roc_auc_score=0. 756805 20%|██ | 3/15 [09:40<34:53, 174. 42s/it, best loss: -0. 7835939030962179]Step 1000 validation: roc_auc_sco re=0. 714572 Step 2000 validation: roc_auc_score=0. 770712 Step 3000 validation: roc_auc_score=0. 777914 Step 4000 validation: roc_auc_score=0. 76923 Step 5000 validation: roc_auc_score=0. 774823 Step 6000 validation: roc_auc_score=0. 775927 Step 7000 validation: roc_auc_score=0. 777054 Step 8000 validation: roc_auc_score=0. 778508 | deepchem.pdf |
27%|██▋ | 4/15 [12:12<30:22, 165. 66s/it, best loss: -0. 7835939030962179]Step 1000 validation: roc_auc_sco re=0. 743939 Step 2000 validation: roc_auc_score=0. 759478 Step 3000 validation: roc_auc_score=0. 738839 Step 4000 validation: roc_auc_score=0. 751084 Step 5000 validation: roc_auc_score=0. 740504 Step 6000 validation: roc_auc_score=0. 753612 Step 7000 validation: roc_auc_score=0. 71802 Step 8000 validation: roc_auc_score=0. 761025 33%|███▎ | 5/15 [17:40<37:21, 224. 16s/it, best loss: -0. 7835939030962179]Step 1000 validation: roc_auc_sco re=0. 74099 Step 2000 validation: roc_auc_score=0. 767516 Step 3000 validation: roc_auc_score=0. 767338 Step 4000 validation: roc_auc_score=0. 775691 Step 5000 validation: roc_auc_score=0. 768731 Step 6000 validation: roc_auc_score=0. 755029 Step 7000 validation: roc_auc_score=0. 767115 Step 8000 validation: roc_auc_score=0. 764744 40%|████ | 6/15 [22:48<37:54, 252. 71s/it, best loss: -0. 7835939030962179]Step 1000 validation: roc_auc_sco re=0. 713761 Step 2000 validation: roc_auc_score=0. 759518 Step 3000 validation: roc_auc_score=0. 765853 Step 4000 validation: roc_auc_score=0. 771976 Step 5000 validation: roc_auc_score=0. 772762 Step 6000 validation: roc_auc_score=0. 773206 Step 7000 validation: roc_auc_score=0. 775565 Step 8000 validation: roc_auc_score=0. 768521 47%|████▋ | 7/15 [27:53<35:58, 269. 84s/it, best loss: -0. 7835939030962179]Step 1000 validation: roc_auc_sco re=0. 717178 Step 2000 validation: roc_auc_score=0. 754258 Step 3000 validation: roc_auc_score=0. 767905 Step 4000 validation: roc_auc_score=0. 762917 Step 5000 validation: roc_auc_score=0. 766162 Step 6000 validation: roc_auc_score=0. 767581 Step 7000 validation: roc_auc_score=0. 770746 Step 8000 validation: roc_auc_score=0. 77597 53%|█████▎ | 8/15 [30:36<27:29, 235. 64s/it, best loss: -0. 7835939030962179]Step 1000 validation: roc_auc_sco re=0. 74314 Step 2000 validation: roc_auc_score=0. 757408 Step 3000 validation: roc_auc_score=0. 76668 Step 4000 validation: roc_auc_score=0. 768104 Step 5000 validation: roc_auc_score=0. 746377 Step 6000 validation: roc_auc_score=0. 745282 Step 7000 validation: roc_auc_score=0. 74113 Step 8000 validation: roc_auc_score=0. 734482 60%|██████ | 9/15 [36:53<28:00, 280. 04s/it, best loss: -0. 7835939030962179]Step 1000 validation: roc_auc_sco re=0. 743204 Step 2000 validation: roc_auc_score=0. 76912 Step 3000 validation: roc_auc_score=0. 769981 Step 4000 validation: roc_auc_score=0. 784163 Step 5000 validation: roc_auc_score=0. 77536 Step 6000 validation: roc_auc_score=0. 779237 Step 7000 validation: roc_auc_score=0. 782344 Step 8000 validation: roc_auc_score=0. 779085 67%|██████▋ | 10/15 [38:23<18:26, 221. 33s/it, best loss: -0. 7841634210268469]Step 1000 validation: roc_auc_sc ore=0. 743565 Step 2000 validation: roc_auc_score=0. 765063 Step 3000 validation: roc_auc_score=0. 75284 Step 4000 validation: roc_auc_score=0. 759978 Step 5000 validation: roc_auc_score=0. 74255 Step 6000 validation: roc_auc_score=0. 721809 Step 7000 validation: roc_auc_score=0. 729863 Step 8000 validation: roc_auc_score=0. 73075 73%|███████▎ | 11/15 [44:07<17:15, 258. 91s/it, best loss: -0. 7841634210268469]Step 1000 validation: roc_auc_sc ore=0. 695949 Step 2000 validation: roc_auc_score=0. 765082 Step 3000 validation: roc_auc_score=0. 756256 Step 4000 validation: roc_auc_score=0. 771923 Step 5000 validation: roc_auc_score=0. 758841 Step 6000 validation: roc_auc_score=0. 759393 Step 7000 validation: roc_auc_score=0. 765971 Step 8000 validation: roc_auc_score=0. 747064 80%|████████ | 12/15 [48:54<13:21, 267. 23s/it, best loss: -0. 7841634210268469]Step 1000 validation: roc_auc_sc ore=0. 757871 Step 2000 validation: roc_auc_score=0. 765296 Step 3000 validation: roc_auc_score=0. 769748 Step 4000 validation: roc_auc_score=0. 776487 Step 5000 validation: roc_auc_score=0. 775009 Step 6000 validation: roc_auc_score=0. 779539 Step 7000 validation: roc_auc_score=0. 763165 Step 8000 validation: roc_auc_score=0. 772093 87%|████████▋ | 13/15 [50:22<07:06, 213. 15s/it, best loss: -0. 7841634210268469]Step 1000 validation: roc_auc_sc ore=0. 720166 | deepchem.pdf |
Step 2000 validation: roc_auc_score=0. 768489 Step 3000 validation: roc_auc_score=0. 782853 Step 4000 validation: roc_auc_score=0. 785556 Step 5000 validation: roc_auc_score=0. 78583 Step 6000 validation: roc_auc_score=0. 786569 Step 7000 validation: roc_auc_score=0. 779249 Step 8000 validation: roc_auc_score=0. 783423 93%|█████████▎| 14/15 [51:52<02:55, 175. 93s/it, best loss: -0. 7865693280913189]Step 1000 validation: roc_auc_sc ore=0. 743232 Step 2000 validation: roc_auc_score=0. 762007 Step 3000 validation: roc_auc_score=0. 771809 Step 4000 validation: roc_auc_score=0. 755023 Step 5000 validation: roc_auc_score=0. 769812 Step 6000 validation: roc_auc_score=0. 769867 Step 7000 validation: roc_auc_score=0. 777354 Step 8000 validation: roc_auc_score=0. 775313 100%|██████████| 15/15 [56:47<00:00, 227. 13s/it, best loss: -0. 7865693280913189] The code below is used to print the best hyperparameters found by the hyperopt. print ( "Best: {} ". format ( best )) Best: {'dropout': 0. 3749846096922802, 'layer_sizes': 0, 'learning_rate': 0. 0007544819475363869} The hyperparameter found here may not be necessarily the best one, but gives a general idea on which parameters are effective. To get mroe accurate results, one has to increase the number of validation epochs and the epochs the model fit. But doing so may increase the time in finding the best hyperparameters. Congratulations! Time to join the Community! Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with Deep Chem, we encourage you to finish the rest of the tutorials in this series. You can also help the Deep Chem community in the following ways: Star Deep Chem on Git Hub This helps build awareness of the Deep Chem project and the tools for open source drug discovery that we're trying to build. Join the Deep Chem Discord The Deep Chem Discord hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation! | deepchem.pdf |
Introduction to Gaussian Processes In the world of cheminformatics and machine learning, models are often trees (random forest, XGBoost, etc. ) or artifical neural networks (deep neural networks, graph convolutional networks, etc. ). These models are known as "Frequentist" models. However, there is another category known as Bayesian models. Today we will be experimenting with a Bayesian model implemented in scikit-learn known as gaussian processes (GP). For a deeper dive on GP, there is a great tutorial paper on how GP works for regression. There is also an academic paper that applies GP to a real world problem. As a short intro, GP allows us to build up our statistical model using an infinite number of Gaussian functions over our n-dimensional space, where n is the number of features. However, we pick these functions based on how well they fit the data we pass it. We end up with a statistical model built from an ensemble of Gaussian functions which can actually vary quite a bit. The result is that for points we have trained the model on, the variance in our ensemble should be very low. For test set points close to the training set points, the variance should be higher but still low as the ensemble was picked to predict well in its neighborhood. For points far from the training set points, however, we did not pick our ensemble of Gaussian functions to fit them so we'd expect the variance in our ensemble to be high. In this way, we end up with a statistical model that allows for a natural generation of uncertainty. Colab This tutorial and the rest in the sequences are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link. O p e n i n C o l a b O p e n i n C o l a b Setup The first step is to get Deep Chem up and running. We recommend using Google Colab to work through this tutorial series. You'll need to run the following commands to get Deep Chem installed on your colab notebook. % pip install --pre deepchem Requirement already satisfied: deepchem in /home/ozone/miniconda3/envs/mol/lib/python3. 7/site-packages (2. 5. 0. de v20210319222130) Requirement already satisfied: scikit-learn in /home/ozone/miniconda3/envs/mol/lib/python3. 7/site-packages (from deepchem) (1. 0. 2) Requirement already satisfied: numpy in /home/ozone/miniconda3/envs/mol/lib/python3. 7/site-packages (from deepch em) (1. 19. 1) Requirement already satisfied: pandas in /home/ozone/miniconda3/envs/mol/lib/python3. 7/site-packages (from deepc hem) (1. 3. 1) Requirement already satisfied: joblib in /home/ozone/miniconda3/envs/mol/lib/python3. 7/site-packages (from deepc hem) (1. 1. 0) Requirement already satisfied: scipy in /home/ozone/miniconda3/envs/mol/lib/python3. 7/site-packages (from deepch em) (1. 6. 2) Requirement already satisfied: python-dateutil>=2. 7. 3 in /home/ozone/miniconda3/envs/mol/lib/python3. 7/site-pack ages (from pandas->deepchem) (2. 8. 2) Requirement already satisfied: pytz>=2017. 3 in /home/ozone/miniconda3/envs/mol/lib/python3. 7/site-packages (from pandas->deepchem) (2021. 3) Requirement already satisfied: six>=1. 5 in /home/ozone/miniconda3/envs/mol/lib/python3. 7/site-packages (from pyt hon-dateutil>=2. 7. 3->pandas->deepchem) (1. 16. 0) Requirement already satisfied: threadpoolctl>=2. 0. 0 in /home/ozone/miniconda3/envs/mol/lib/python3. 7/site-packag es (from scikit-learn->deepchem) (2. 2. 0) Gaussian Processes As stated earlier, GP is already implemented in scikit-learn so we will be using Deep Chem's scikit-learn wrapper. Sklearn Model is a subclass of Deep Chem's Model class. It acts as a wrapper around a sklearn. base. Base Estimator. Here we import deepchem and the GP regressor model from sklearn. import deepchem as dc from sklearn. gaussian_process import Gaussian Process Regressor from sklearn. gaussian_process. kernels import RBF, White Kernel import numpy as np import matplotlib. pyplot as plt Loading data | deepchem.pdf |
Next we need a dataset that presents a regression problem. For this tutorial we will be using the BACE dataset from Molecule Net. tasks, datasets, transformers = dc. molnet. load_bace_regression ( featurizer = 'ecfp', splitter = 'random' ) train_dataset, valid_dataset, test_dataset = datasets I always like to get a close look at what the objects in my code are storing. We see that tasks is a list of tasks that we are trying to predict. The transformer is a Normalization Transformer that normalizes the outputs (y values) of the dataset. print ( f 'The tasks are: { tasks } ' ) print ( f 'The transformers are: { transformers } ' ) print ( f 'The transformer normalizes the outputs (y values): { transformers [ 0 ]. transform_y } ' ) The tasks are: ['p IC50'] The transformers are: [<deepchem. trans. transformers. Normalization Transformer object at 0x7fc04401b190>] The transformer normalizes the outputs (y values): True Here we see that the data has already been split into a training set, a validation set, and a test set. We will train the model on the training set and test the accuracy of the model on the test set. If we were to do any hyperparameter tuning, we would use the validation set. The split was ~80/10/10 train/valid/test. print ( train_dataset ) print ( valid_dataset ) print ( test_dataset ) <Disk Dataset X. shape: (1210, 1024), y. shape: (1210, 1), w. shape: (1210, 1), task_names: ['p IC50']> <Disk Dataset X. shape: (151, 1024), y. shape: (151, 1), w. shape: (151, 1), ids: ['Fc1ncccc1-c1cc(ccc1)C1(N=C(N)N(C )C1=O)c1cn(nc1)CC(CC)CC' 'S1(=O)(=O)N(c2cc(cc3n(cc(CC1)c23)CC)C(=O)NC(Cc1ccccc1)C(=O)C[NH2+]C1CCOCC1)C' 's1ccnc1-c1cc(ccc1)CC(NC(=O)[C@@H](OC)C)C(O)C[NH2+]C1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2' ... 'S(=O)(=O)(Nc1cc(cc(c1)C(C)(C)C)C1([NH2+]CC(O)C(NC(=O)C)Cc2cc(F)cc(F)c2)CCCCC1)C' 'O=C1N(C)C(=N[C@]1(c1cc(nc(c1)CC)CC)c1cc(ccc1)-c1cncnc1)N' 'Clc1cc2CC(N=C(NC(Cc3ccccc3)C=3NC(=O)c4c(N=3)cccc4)c2cc1)(C)C'], task_names: ['p IC50']> <Disk Dataset X. shape: (152, 1024), y. shape: (152, 1), w. shape: (152, 1), ids: ['Clc1ccc(cc1)CC(NC(=O)C)C(O)C[NH2 +]C1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2' 'Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(Oc2ccc(F)cc2)c1)C(=O)N(CCC)CCC)C(O)C[NH2+]Cc1cc(OC)ccc1' 'O1c2c(cc(cc2)C2CCCCC2)C2(N=C(N)N(C)C2=O)CC1(C)C' ... 'S(=O)(=O)(N(C)c1cc(cc(c1)COCC([NH3+])(Cc1ccccc1)C(F)F)C(=O)NC(C)c1ccc(F)cc1)C' 'O1CCCC1CN1C(=O)C(N=C1N)(C1CCCCC1)c1ccccc1' 'Fc1cc(cc(c1)C#C)CC(NC(=O)COC)C(O)C[NH2+]C1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2'], task_names: ['p IC50']> Using the Sklearn Model Here we first create the model using the Gaussian Process Regressor we imported from sklearn. Then we wrap it in Deep Chem's Sklearn Model. To learn more about the model, you can either read the sklearn API or run help(Gaussian Process Regressor) in a code block. As you see, the values I picked for the parameters seem awfully specific. This is because I needed to do some hyperparameter tuning beforehand to get model that wasn't wildly overfitting the training set. You can learn more about how I tuned the model in the Appendix at the end of this tutorial. output_variance = 7. 908735015054668 length_scale = 6. 452349252677817 noise_level = 0. 10475507755839343 kernel = output_variance ** 2 * RBF ( length_scale = length_scale, length_scale_bounds = 'fixed' ) + White Kernel ( noise_level alpha = 4. 989499481123432e-09 sklearn_gpr = Gaussian Process Regressor ( kernel = kernel, alpha = alpha ) model = dc. models. Sklearn Model ( sklearn_gpr ) Then we fit our model to the data and see how it performs both on the training set and on the test set. model. fit ( train_dataset ) metric1 = dc. metrics. Metric ( dc. metrics. mean_squared_error ) metric2 = dc. metrics. Metric ( dc. metrics. r2_score ) print ( f 'Training set score: { model. evaluate ( train_dataset, [ metric1, metric2 ]) } ' ) print ( f 'Test set score: { model. evaluate ( test_dataset, [ metric1, metric2 ]) } ' ) Training set score: {'mean_squared_error': 0. 0457129375800123, 'r2_score': 0. 9542870624199877} Test set score: {'mean_squared_error': 0. 20503945381118496, 'r2_score': 0. 7850242035806018} Analyzing the Results We can also visualize how well the predicted values match up to the measured values. First we need a function that | deepchem.pdf |
allows us to obtain both the mean predicted value and the standard deviation of the value. This is done by sampling 100 predictions from each set of inputs X and calculating the mean and standard deviation. def predict_with_error ( dc_model, X, y_transformer ): samples = model. model. sample_y ( X, 100 ) means = y_transformer. untransform ( np. mean ( samples, axis = 1 )) stds = y_transformer. y_stds [ 0 ] * np. std ( samples, axis = 1 ) return means, stds For our training set, we see a pretty good correlation between the measured values (x-axis) and the predicted values (y-axis). Note that we use the transformer from earlier to untransform our predicted values. y_meas_train = transformers [ 0 ]. untransform ( train_dataset. y ) y_pred_train, y_pred_train_stds = predict_with_error ( model, train_dataset. X, transformers [ 0 ]) plt. xlim ([ 2. 5, 10. 5 ]) plt. ylim ([ 2. 5, 10. 5 ]) plt. scatter ( y_meas_train, y_pred_train ) <matplotlib. collections. Path Collection at 0x7fc0431b45d0> We now do the same for our test set. We see a fairly good correlation! However, it is certainly not as tight. This is reflected in the difference between the R2 scores calculated above. y_meas_test = transformers [ 0 ]. untransform ( test_dataset. y ) y_pred_test, y_pred_test_stds = predict_with_error ( model, test_dataset. X, transformers [ 0 ]) plt. xlim ([ 2. 5, 10. 5 ]) plt. ylim ([ 2. 5, 10. 5 ]) plt. scatter ( y_meas_test, y_pred_test ) <matplotlib. collections. Path Collection at 0x7fc04023b590> We can also write a function to calculate how many of the predicted values fall within the predicted error range. This is done by counting up how many samples have a true error smaller than its standard deviation calculated earlier. One standard deviation is a 68% confidence interval. def percent_within_std ( y_meas, y_pred, y_std ): assert len ( y_meas ) == len ( y_pred ) and len ( y_meas ) == len ( y_std ), 'length of y_meas and y_pred must be the same' count_within_error = 0 for i in range ( len ( y_meas )): if abs ( y_meas [ i ][ 0 ]-y_pred [ i ]) < y_std [ i ]: count_within_error += 1 return count_within_error / len ( y_meas ) For the train set, >90% of the samples are within a standard deviation. In comparison, only ~70% of the samples are | deepchem.pdf |
within a standard deviation for the test set. A standard deviation is a 68% confidence interval so we see that for the training set, the uncertainty is close. However, this model overpredicts uncertainty on the training set. percent_within_std ( y_meas_train, y_pred_train, y_pred_train_stds ) 0. 9355371900826446 percent_within_std ( y_meas_test, y_pred_test, y_pred_test_stds ) 0. 7368421052631579 We can also take a look at the distributions of the standard deviations for the test set predictions. We see a very roughly Gaussian distribution in the predicted errors. plt. hist ( y_pred_test_stds ) plt. show () For now, this is the end of our tutorial. We plan to follow up soon with a deeper dive into uncertainty estimation and in particular, calibrated uncertainty estimation. We will see you then! Appendix: Hyperparameter Optimization As hyperparameter optimization is outside the scope of this tutorial, I will not explain how to use Optuna to tune hyperparameters. But the code is still included for the sake of completeness. % pip install optuna import optuna def get_model ( trial ): output_variance = trial. suggest_float ( 'output_variance', 0. 1, 10, log = True ) length_scale = trial. suggest_float ( 'length_scale', 1e-5, 1e5, log = True ) noise_level = trial. suggest_float ( 'noise_level', 1e-5, 1e5, log = True ) params = { 'kernel' : output_variance ** 2 * RBF ( length_scale = length_scale, length_scale_bounds = 'fixed' ) + White Kernel ( noise_level 'alpha' : trial. suggest_float ( 'alpha', 1e-12, 1e-5, log = True ), } sklearn_gpr = Gaussian Process Regressor ( ** params ) return dc. models. Sklearn Model ( sklearn_gpr ) def objective ( trial ): model = get_model ( trial ) model. fit ( train_dataset ) metric = dc. metrics. Metric ( dc. metrics. mean_squared_error ) return model. evaluate ( valid_dataset, [ metric ])[ 'mean_squared_error' ] study = optuna. create_study ( direction = 'minimize' ) study. optimize ( objective, n_trials = 100 ) print ( study. best_params ) {'output_variance': 0. 38974570882583015, 'length_scale': 5. 375387643239208, 'noise_level': 0. 0016265333497286342, 'alpha': 1. 1273318360324618e-11} | deepchem.pdf |
Pytorch-Lightning Integration for Deep Chem Models In this tutorial we will go through how to setup a deepchem model inside the pytorch-lightning framework. Lightning is a pytorch framework which simplifies the process of experimenting with pytorch models easier. A few key functionalities offered by pytorch lightning which deepchem users can find useful are: 1. Multi-gpu training functionalities: pytorch-lightning provides easy multi-gpu, multi-node training. It also simplifies the process of launching multi-gpu, multi-node jobs across different cluster infrastructure, e. g. AWS, slurm based clusters. 2. Reducing boilerplate pytorch code: lightning takes care of details like, optimizer. zero_grad(), model. train(), model. eval(). Lightning also provides experiment logging functionality, for e. g. irrespective of training on CPU, GPU, multi-nodes the user can use the method self. log inside the trainer and it will appropriately log the metrics. 3. Features that can speed up training: half-precision training, gradient checkpointing, code profiling. O p e n i n C o l a b O p e n i n C o l a b Setup This notebook assumes that you have already installed deepchem, if you have not follow the instructions at the deepchem installation page: https://deepchem. readthedocs. io/en/latest/get_started/installation. html. 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python_version < "3. 10"->markdown>=2. 6. 8->tensorboard>=2. 2. 0->pytorch_lig htning) (3. 7. 0) Installing collected packages: future Successfully installed future-0. 18. 2 Import the relevant packages. import deepchem as dc from deepchem. models import GCNModel | deepchem.pdf |
import pytorch_lightning as pl import torch from torch. nn import functional as F from torch import nn import pytorch_lightning as pl from pytorch_lightning. core. lightning import Lightning Module from torch. optim import Adam import numpy as np import torch Deepchem Example Below we show an example of a Graph Convolution Network (GCN). Note that this is a simple example which uses a GCNModel to predict the label from an input sequence. We do not showcase the complete functionality of deepchem in this example as we want to restructure the deepchem code and adapt it so that it can be easily plugged into pytorch-lightning. This example was inspired from the GCNModel documentation present here. Prepare the dataset : for training our deepchem models we need a dataset that we can use to train the model. Below we prepare a sample dataset for the purposes of this tutorial. Below we also directly use the featurized to encode examples for the dataset. smiles = [ "C1CCC1", "CCC" ] labels = [ 0., 1. ] featurizer = dc. feat. Mol Graph Conv Featurizer () X = featurizer. featurize ( smiles ) dataset = dc. data. Numpy Dataset ( X = X, y = labels ) Setup the model : now we initialize the Graph Convolutional Network model that we will use in our training. model = GCNModel ( mode = 'classification', n_tasks = 1, batch_size = 2, learning_rate = 0. 001 ) [16:00:37] /Users/princychahal/Documents/github/dgl/src/runtime/tensordispatch. cc:43: Tensor Dispatcher: dlopen f ailed: Using backend: pytorch dlopen(/Users/princychahal/mambaforge/envs/keras_try_5/lib/python3. 8/site-packages/dgl-0. 8-py3. 8-macosx-11. 0-arm 64. egg/dgl/tensoradapter/pytorch/libtensoradapter_pytorch_1. 10. 2. dylib, 1): image not found Train the model : fit the model on our training dataset, also specify the number of epochs to run. loss = model. fit ( dataset, nb_epoch = 5 ) print ( loss ) 0. 18830760717391967 /Users/princychahal/mambaforge/envs/keras_try_5/lib/python3. 8/site-packages/torch/autocast_mode. py:141: User Warn ing: User provided device_type of 'cuda', but CUDA is not available. Disabling warnings. warn('User provided device_type of \'cuda\', but CUDA is not available. Disabling') Pytorch-Lightning + Deepchem example Now we will look at an example of the GCN model adapt for Pytorch-Lightning. For using Pytorch-Lightning there are two important components: 1. Lightning Data Module : This module defines who the data is prepared and fed into the model so that the model can use it for training. The module defines the train dataloader function which are directly used by the trainer to generate data for the Lightning Module. To learn more about the Lightning Data Module refer to the datamodules documentation. 2. Lightning Module : This module defines the training, validation steps for our model. We can use this module to initialize our model based on the hyperparameters. There are a number of boilerplate functions which we use directly to track our experiments, for example we can save all the hyperparameters that we used for training using the self. save_hyperparameters() method. For more details on how to use this module refer to the lightningmodules documentation. Setup the torch dataset : Note that here we need to create a custome Smiles Dataset so that we can easily interface with the deepchem featurizers. For this interface we need to define a collate method so that we can create batches for the dataset. # prepare Lightning Data Module class Smiles Dataset ( torch. utils. data. Dataset ): def __init__ ( self, smiles, labels ): assert len ( smiles ) == len ( labels ) | deepchem.pdf |
featurizer = dc. feat. Mol Graph Conv Featurizer () X = featurizer. featurize ( smiles ) self. _samples = dc. data. Numpy Dataset ( X = X, y = labels ) def __len__ ( self ): return len ( self. _samples ) def __getitem__ ( self, index ): return ( self. _samples. X [ index ], self. _samples. y [ index ], self. _samples. w [ index ], ) class Smiles Dataset Batch : def __init__ ( self, batch ): X = [ np. array ([ b [ 0 ] for b in batch ])] y = [ np. array ([ b [ 1 ] for b in batch ])] w = [ np. array ([ b [ 2 ] for b in batch ])] self. batch_list = [ X, y, w ] def collate_smiles_dataset_wrapper ( batch ): return Smiles Dataset Batch ( batch ) Create the GCN specific lightning module : in this part we use an object of the Smiles Dataset created above to create the Smiles Dataset Module class Smiles Dataset Module ( pl. Lightning Data Module ): def __init__ ( self, train_smiles, train_labels, batch_size ): super (). __init__ () self. _train_smiles = train_smiles self. _train_labels = train_labels self. _batch_size = batch_size def setup ( self, stage ): self. train_dataset = Smiles Dataset ( self. _train_smiles, self. _train_labels, ) def train_dataloader ( self ): return torch. utils. data. Data Loader ( self. train_dataset, batch_size = self. _batch_size, collate_fn = collate_smiles_dataset_wrapper, shuffle = True, ) Create the lightning module : in this part we create the GCN specific lightning module. This class specifies the logic flow for the training step. We also create the required models, optimizers and losses for the training flow. # prepare the Lightning Module class GCNModule ( pl. Lightning Module ): def __init__ ( self, mode, n_tasks, learning_rate ): super (). __init__ () self. save_hyperparameters ( "mode", "n_tasks", "learning_rate", ) self. gcn_model = GCNModel ( mode = self. hparams. mode, n_tasks = self. hparams. n_tasks, learning_rate = self. hparams. learning_rate, ) self. pt_model = self. gcn_model. model self. loss = self. gcn_model. _loss_fn def configure_optimizers ( self ): return self. gcn_model. optimizer. _create_pytorch_optimizer ( self. pt_model. parameters (), ) def training_step ( self, batch, batch_idx ): batch = batch. batch_list inputs, labels, weights = self. gcn_model. _prepare_batch ( batch ) outputs = self. pt_model ( inputs ) if isinstance ( outputs, torch. Tensor ): outputs = [ outputs ] | deepchem.pdf |
if self. gcn_model. _loss_outputs is not None : outputs = [ outputs [ i ] for i in self. gcn_model. _loss_outputs ] loss_outputs = self. loss ( outputs, labels, weights ) self. log ( "train_loss", loss_outputs, on_epoch = True, sync_dist = True, reduce_fx = "mean", prog_bar = True, ) return loss_outputs Create the relevant objects # create module objects smiles_datasetmodule = Smiles Dataset Module ( train_smiles = [ "C1CCC1", "CCC", "C1CCC1", "CCC", "C1CCC1", "CCC", "C1CCC1", "CCC", "C1CCC1", "CCC" ], train_labels = [ 0., 1., 0., 1., 0., 1., 0., 1., 0., 1. ], batch_size = 2, ) gcnmodule = GCNModule ( mode = "classification", n_tasks = 1, learning_rate = 1e-3, ) Lightning Trainer Trainer is the wrapper which builds on top of the Lightning Data Module and Lightning Module. When constructing the lightning trainer you can also specify the number of epochs, max-steps to run, number of GPUs, number of nodes to be used for trainer. Lightning trainer acts as a wrapper over your distributed training setup and this way you are able to build your models in a way you would build them in a simple way for your local runs. trainer = pl. Trainer ( max_epochs = 5, ) GPU available: False, used: False TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs Call the fit function to run model training # train trainer. fit ( model = gcnmodule, datamodule = smiles_datasetmodule, ) | Name | Type | Params----------------------------------0 | pt_model | GCN | 29. 4 K----------------------------------29. 4 K Trainable params 0 Non-trainable params 29. 4 K Total params 0. 118 Total estimated model params size (MB) /Users/princychahal/mambaforge/envs/keras_try_5/lib/python3. 8/site-packages/pytorch_lightning/trainer/data_loadi ng. py:132: User Warning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `Data Loader` init to improve performance. rank_zero_warn( /Users/princychahal/mambaforge/envs/keras_try_5/lib/python3. 8/site-packages/pytorch_lightning/trainer/data_loadi ng. py:428: User Warning: The number of training samples (5) is smaller than the logging interval Trainer(log_ever y_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch. rank_zero_warn( Training: 0it [00:00, ?it/s] | deepchem.pdf |
Compiling Deepchem Torch Models Py Torch introduced the torch. compile() function in Py Torch 2. 0 to allow faster training and inference of the models by compiling Py Torch code into optimised kernels using a JIT(Just in Time) compiler. Different models show varying levels of improvement in run times depending on their architecture and batch size when compiled. Compared to existing methods like Torch Script or FX tracing, compile() offers advantages such as the ability to handle arbitrary Python code and conditional graph-breaking flow of the inputs to the models. This allows compile() to work with minimal or no code modification to the model. Using this feature, Deep Chem users can efficiently run Py Torch models and achieve significant performance gains. NOTE: Deep Chem contains many models with varying architecture and complexity. Not all models will show significant improvements in run times when compiled. It is recommended to test the models with and without compilation to determine the performance improvements. Colab This tutorial and the rest in this sequence can be done in Google colab. If you'd like to open this notebook in colab, you can use the following link. O p e n i n C o l a b O p e n i n C o l a b Compilation Process This section gives an introductory explanation about the compilation process of Py Torch models and assumes prior knowledge about forward pass, backward pass and computational graphs in neural networks. If you're unfamiliar with these concepts, you can refer to these slides for a basic understanding. Alternatively, you can proceed to the next section to learn how to compile and benchmark Deep Chem models without delving into the internal details of the compilation process. Image taken from Py Torch2. 0 Introductory Blog The compilation process is split into multiple steps which uses many new technologies that were introduced in Py Torch 2. 0. The process is as follows: 1. Graph Acquisition: During the compilation process, Torch Dynamo and AOTAutograd are used for capturing the forward and backward pass graphs respectively. AOTAutograd allows the backward graph to be captured ahead of time without needing a backward pass to be performed. 2. Graph Lowering: The captured graph that could be composed of the 2000+ Py Torch operators is lowered into a collection of ~250 Prim and ~750 ATen operators. 3. Graph Compilation: In this step optimised low-level kernels are generated for the target accelerator using a suitable backend compiler. Torch Inductor is the default backend compiler used for this purpose. | deepchem.pdf |
Deepchem uses the torch. compile() function that implements all the above steps internally to compile the models. The compiled model can be used for training, evaluation and inference. For more information on the compilation process, refer to Py Torch2. 0 Introductory Blog that does a deep dive into the compilation process, technical decisions and future features for the compile function. You can also refer to the Huggingface blog, Optimize inference using torch. compile() that benchmarks many common Py Torch models and shows the performance improvements when compiled. Compiling Models The compile function is only available in Deep Chem for models that use Py Torch as the backend (i. e inherits Torch Model class). You can see the complete list of models that are available in Deep Chem and their backends in the Deep Chem Documentation here. This tutorial contains the steps to load a Deep Chem model, compile it and evaluate the performance improvements when compiled for both training and inference. Refer to the documentation of Deep Chem's compile function to read more about the different parameters you can pass to the function and their usage. If you just want to compile the model, you can add the line model. compile() after initialising the model. You DO NOT have to make any changes to the rest of your code. Some of the things to keep in mind when compiling models are: 1. Selecting the right mode: The modes can be default, reduce-overhead, max-autotune or max-autotune-no-cudagraphs. Out of this reduce-overhead and max-autotune modes requires triton to be installed. Refer to the Py Torch docs on torch. compile for more information on the modes. 2. Setting fullgraph parameter: If True (default False ), torch. compile will require that the entire function be capturable into a single graph. If this is not possible (that is, if there are graph breaks), then the function will raise an error. 3. Experimenting with different parameter configuration: Different parameter configurations can give different speedups based on the model, batch size and the device used for training/inference. Experiment with a few parameter combinations to check which one gives better results. In this tutorial, we will be using DMPNN model and Freesolv Dataset for training and inference of the models. ! pip install --pre deepchem ! pip install torch_geometric #required for DMPNN model ! pip install triton #required for reduce-overhead mode | deepchem.pdf |
Collecting deepchem Downloading deepchem-2. 8. 1. dev20240624214143-py3-none-any. whl (1. 1 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1. 1/1. 1 MB 6. 9 MB/s eta 0:00:00 Requirement already satisfied: joblib in /usr/local/lib/python3. 10/dist-packages (from deepchem) (1. 4. 2) Requirement already satisfied: numpy<2 in /usr/local/lib/python3. 10/dist-packages (from deepchem) (1. 25. 2) Requirement already satisfied: pandas in /usr/local/lib/python3. 10/dist-packages (from deepchem) (2. 0. 3) Requirement already satisfied: scikit-learn in /usr/local/lib/python3. 10/dist-packages (from deepchem) (1. 2. 2) Requirement already satisfied: sympy in /usr/local/lib/python3. 10/dist-packages (from deepchem) (1. 12. 1) Requirement already satisfied: scipy>=1. 10. 1 in /usr/local/lib/python3. 10/dist-packages (from deepchem) (1. 11. 4) Collecting rdkit (from deepchem) Downloading rdkit-2024. 3. 1-cp310-cp310-manylinux_2_17_x86_64. manylinux2014_x86_64. whl (35. 1 MB) 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Installing collected packages: rdkit, deepchem Successfully installed deepchem-2. 8. 1. dev20240624214143 rdkit-2024. 3. 1 Collecting torch_geometric Downloading torch_geometric-2. 5. 3-py3-none-any. whl (1. 1 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1. 1/1. 1 MB 10. 8 MB/s eta 0:00:00 Requirement already satisfied: tqdm in /usr/local/lib/python3. 10/dist-packages (from torch_geometric) (4. 66. 4) Requirement already satisfied: numpy in /usr/local/lib/python3. 10/dist-packages (from torch_geometric) (1. 25. 2) Requirement already satisfied: scipy in /usr/local/lib/python3. 10/dist-packages (from torch_geometric) (1. 11. 4) Requirement already satisfied: fsspec in /usr/local/lib/python3. 10/dist-packages (from torch_geometric) (2023. 6. 0) Requirement already satisfied: jinja2 in /usr/local/lib/python3. 10/dist-packages (from torch_geometric) (3. 1. 4) Requirement already satisfied: aiohttp in /usr/local/lib/python3. 10/dist-packages (from torch_geometric) (3. 9. 5) Requirement 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Requirement already satisfied: joblib>=1. 1. 1 in /usr/local/lib/python3. 10/dist-packages (from scikit-learn->torc h_geometric) (1. 4. 2) Requirement already satisfied: threadpoolctl>=2. 0. 0 in /usr/local/lib/python3. 10/dist-packages (from scikit-lear n->torch_geometric) (3. 5. 0) Installing collected packages: torch_geometric Successfully installed torch_geometric-2. 5. 3 Requirement already satisfied: triton in /usr/local/lib/python3. 10/dist-packages (2. 3. 0) Requirement already satisfied: filelock in /usr/local/lib/python3. 10/dist-packages (from triton) (3. 15. 3) import torch import datetime import numpy as np import deepchem as dc import matplotlib. pyplot as plt | deepchem.pdf |
WARNING:deepchem. feat. molecule_featurizers. rdkit_descriptors:No normalization for SPS. Feature removed! WARNING:deepchem. feat. molecule_featurizers. rdkit_descriptors:No normalization for Avg Ipc. Feature removed! WARNING:tensorflow:From /usr/local/lib/python3. 10/dist-packages/tensorflow/python/util/deprecation. py:588: calli ng function (from tensorflow. python. eager. polymorphic_function. polymorphic_function) with experimental_relax_sha pes is deprecated and will be removed in a future version. Instructions for updating: experimental_relax_shapes is deprecated, use reduce_retracing instead WARNING:deepchem. models. torch_models:Skipped loading modules with pytorch-geometric dependency, missing a depend ency. No module named 'dgl' WARNING:deepchem. models:Skipped loading modules with pytorch-lightning dependency, missing a dependency. No modu le named 'lightning' WARNING:deepchem. models:Skipped loading some Jax models, missing a dependency. No module named 'haiku' torch. _dynamo. config. cache_size_limit = 64 tasks, datasets, transformers = dc. molnet. load_freesolv ( featurizer = dc. feat. DMPNNFeaturizer (), splitter = 'random' ) train_dataset, valid_dataset, test_dataset = datasets len ( train_dataset ), len ( valid_dataset ), len ( test_dataset ) model = dc. models. DMPNNModel () The below line is the only addition you have to make to the code for compiling the model. You can pass in the other arguments too to the compile() function if they are required. model. compile () /usr/lib/python3. 10/multiprocessing/popen_fork. py:66: Runtime Warning: os. fork() was called. os. fork() is incompa tible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock. self. pid = os. fork() model. fit ( train_dataset, nb_epoch = 10 ) metrics = [ dc. metrics. Metric ( dc. metrics. mean_squared_error )] print ( f "Training MSE: { model. evaluate ( train_dataset, metrics = metrics ) } " ) print ( f "Validation MSE: { model. evaluate ( valid_dataset, metrics = metrics ) } " ) print ( f "Test MSE: { model. evaluate ( test_dataset, metrics = metrics ) } " ) Training MSE: {'mean_squared_error': 0. 04699941161198689} Validation MSE: {'mean_squared_error': 0. 18010469643557037} Test MSE: {'mean_squared_error': 0. 043559911545479245} Benchmarking model Speedups This section contains the steps for benchmarking the performance of models after compilation process for both training and inference. We are using the same model(DMPNN) and dataset(Fre Solv) in this section too. The steps for compilation and benchmarking is same for other models as well. To account for the initial performance overhead of kernel compilation in compiled models, median values are employed as the performance metric throughout the tutorial for calculating speedup. The below two functions, time_torch_function and get_time_track_callback can be used for tracking the time taken for inference and training respectively. The implementation of time_torch_function is taken from the Py Torch official torch. compile tutorial here. We use get_time_track_callback to make a callback that can track the time taken for each batch during training as Deep Chem does not provide a direct way to track the time taken per batch during training. We can use this callback by passing it as an argument to model. fit() function. def time_torch_function ( fn ): start = torch. cuda. Event ( enable_timing = True ) end = torch. cuda. Event ( enable_timing = True ) start. record () result = fn () end. record () torch. cuda. synchronize () return result, start. elapsed_time ( end ) / 1000 track_dict = {} prev_time_dict = {} def get_time_track_callback ( track_dict, track_name, track_interval ): track_dict [ track_name ] = [] prev_time_dict [ track_name ] = datetime. datetime. now () def callback ( model, step ): if step % track_interval == 0 : elapsed_time = datetime. datetime. now () - prev_time_dict [ track_name ] track_dict [ track_name ]. append ( elapsed_time. total_seconds ()) prev_time_dict [ track_name ] = datetime. datetime. now () | deepchem.pdf |
return callback Tracking Training Time model = dc. models. DMPNNModel () model_compiled = dc. models. DMPNNModel () model_compiled. compile ( mode = 'reduce-overhead' ) track_interval = 20 eager_dict_name = "eager_train" compiled_dict_name = "compiled_train" eager_train_callback = get_time_track_callback ( track_dict, eager_dict_name, track_interval ) model. fit ( train_dataset, nb_epoch = 10, callbacks = [ eager_train_callback ]) compiled_train_callback = get_time_track_callback ( track_dict, compiled_dict_name, track_interval ) model_compiled. fit ( train_dataset, nb_epoch = 10, callbacks = [ compiled_train_callback ]) 0. 06506308714548746 eager_train_times = track_dict [ eager_dict_name ] compiled_train_times = track_dict [ compiled_dict_name ] print ( f "Eager Times (first 15): { [ f ' { t :. 3f } ' for t in eager_train_times [: 15 ]] } " ) print ( f "Compiled Times (first 15): { [ f ' { t :. 3f } ' for t in compiled_train_times [: 15 ]] } " ) print ( f "Total Eager Time: { sum ( eager_train_times ) } " ) print ( f "Total Compiled Time: { sum ( compiled_train_times ) } " ) print ( f "Eager Median: { np. median ( eager_train_times ) } " ) print ( f "Compiled Median: { np. median ( compiled_train_times ) } " ) print ( f "Median Speedup: { (( np. median ( eager_train_times ) / np. median ( compiled_train_times )) - 1 ) * 100 :. 2f } %" ) Eager Times (first 15): ['1. 067', '0. 112', '0. 093', '0. 097', '0. 102', '0. 098', '0. 095', '0. 097', '0. 099', '0. 098 ', '0. 097', '0. 103', '0. 095', '0. 103', '0. 096'] Compiled Times (first 15): ['29. 184', '21. 463', '11. 503', '13. 742', '1. 951', '5. 595', '7. 568', '8. 201', '7. 761', '0. 083', '7. 087', '2. 421', '1. 961', '0. 079', '1. 948'] Total Eager Time: 29. 176121000000023 Total Compiled Time: 243. 32460400000022 Eager Median: 0. 100118 Compiled Median: 0. 0843535 Median Speedup: 18. 69% x_vals = np. arange ( 1, len ( eager_train_times ) + 1 ) * track_interval plt. plot ( x_vals, eager_train_times, label = "Eager" ) plt. plot ( x_vals, compiled_train_times, label = "Compiled" ) plt. yscale ( 'log', base = 10 ) plt. ylabel ( 'Time (s)' ) plt. xlabel ( 'Batch Iteration' ) plt. legend () plt. show () Looking at the graph, there is a significant difference in the time taken for the compiled and uncompiled versions of the model for the starting many steps. After that the time taken by the compiled model stabilises below the uncompiled model. This is because the compilation is done JIT when the model is first run and the optimized kernels are generated after a few passes. | deepchem.pdf |
Tracking Inference Time model = dc. models. DMPNNModel () model_compiled = dc. models. DMPNNModel () model_compiled. compile ( mode = 'reduce-overhead' ) iters = 100 eager_predict_times = [] compiled_predict_times = [] for i in range ( iters ): for X, y, w, ids in test_dataset. iterbatches ( 64, pad_batches = True ): with torch. no_grad (): _, eager_time = time_torch_function ( lambda : model. predict_on_batch ( X )) _, compiled_time = time_torch_function ( lambda : model_compiled. predict_on_batch ( X )) eager_predict_times. append ( eager_time ) compiled_predict_times. append ( compiled_time ) print ( f "Eager Times (first 15): { [ f ' { t :. 3f } ' for t in eager_predict_times [: 15 ]] } " ) print ( f "Compiled Times (first 15): { [ f ' { t :. 3f } ' for t in compiled_predict_times [: 15 ]] } " ) print ( f "Total Eager Time: { sum ( eager_predict_times ) } " ) print ( f "Total Compiled Time: { sum ( compiled_predict_times ) } " ) print ( f "Eager Median: { np. median ( eager_predict_times ) } " ) print ( f "Compiled Median: { np. median ( compiled_predict_times ) } " ) print ( f "Median Speedup: { (( np. median ( eager_predict_times ) / np. median ( compiled_predict_times )) - 1 ) * 100 :. 2f } %" ) Eager Times (first 15): ['0. 170', '0. 173', '0. 161', '0. 160', '0. 160', '0. 165', '0. 158', '0. 159', '0. 164', '0. 161 ', '0. 162', '0. 154', '0. 159', '0. 161', '0. 162'] Compiled Times (first 15): ['47. 617', '1. 168', '26. 927', '0. 127', '0. 134', '0. 138', '0. 130', '0. 130', '0. 133', ' 0. 125', '0. 130', '0. 132', '0. 139', '0. 128', '0. 133'] Total Eager Time: 35. 297711242675796 Total Compiled Time: 104. 20891365814221 Eager Median: 0. 1617226104736328 Compiled Median: 0. 1332385482788086 Median Speedup: 21. 38% plt. plot ( eager_predict_times, label = "Eager" ) plt. plot ( compiled_predict_times, label = "Compiled" ) plt. ylabel ( 'Time (s)' ) plt. xlabel ( 'Batch Iteration' ) plt. yscale ( 'log', base = 10 ) plt. legend () <matplotlib. legend. Legend at 0x7c7a040c9c30> As with the results we got training, the first few runs for inference also takes significantly more time due to the same reason as mentioned before. Congratulations! Time to join the Community! Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with Deep Chem, we encourage you to finish the rest of the tutorials in this series. You can also help the | deepchem.pdf |
Deep Chem community in the following ways: Star Deep Chem on Git Hub This helps build awareness of the Deep Chem project and the tools for open source drug discovery that we're trying to build. Join the Deep Chem Discord The Deep Chem Discord hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation! | deepchem.pdf |
Molecular Fingerprints Molecules can be represented in many ways. This tutorial introduces a type of representation called a "molecular fingerprint". It is a very simple representation that often works well for small drug-like molecules. Colab This tutorial and the rest in this sequence can be done in Google colab. If you'd like to open this notebook in colab, you can use the following link. O p e n i n C o l a b O p e n i n C o l a b ! pip install --pre deepchem We can now import the deepchem package to play with. import deepchem as dc dc. __version__ '2. 4. 0-rc1. dev' What is a Fingerprint? Deep learning models almost always take arrays of numbers as their inputs. If we want to process molecules with them, we somehow need to represent each molecule as one or more arrays of numbers. Many (but not all) types of models require their inputs to have a fixed size. This can be a challenge for molecules, since different molecules have different numbers of atoms. If we want to use these types of models, we somehow need to represent variable sized molecules with fixed sized arrays. Fingerprints are designed to address these problems. A fingerprint is a fixed length array, where different elements indicate the presence of different features in the molecule. If two molecules have similar fingerprints, that indicates they contain many of the same features, and therefore will likely have similar chemistry. Deep Chem supports a particular type of fingerprint called an "Extended Connectivity Fingerprint", or "ECFP" for short. They also are sometimes called "circular fingerprints". The ECFP algorithm begins by classifying atoms based only on their direct properties and bonds. Each unique pattern is a feature. For example, "carbon atom bonded to two hydrogens and two heavy atoms" would be a feature, and a particular element of the fingerprint is set to 1 for any molecule that contains that feature. It then iteratively identifies new features by looking at larger circular neighborhoods. One specific feature bonded to two other specific features becomes a higher level feature, and the corresponding element is set for any molecule that contains it. This continues for a fixed number of iterations, most often two. Let's take a look at a dataset that has been featurized with ECFP. tasks, datasets, transformers = dc. molnet. load_tox21 ( featurizer = 'ECFP' ) train_dataset, valid_dataset, test_dataset = datasets print ( train_dataset ) <Disk Dataset X. shape: (6264, 1024), y. shape: (6264, 12), w. shape: (6264, 12), task_names: ['NR-AR' 'NR-AR-LBD' ' NR-Ah R' ... 'SR-HSE' 'SR-MMP' 'SR-p53']> The feature array X has shape (6264, 1024). That means there are 6264 samples in the training set. Each one is represented by a fingerprint of length 1024. Also notice that the label array y has shape (6264, 12): this is a multitask dataset. Tox21 contains information about the toxicity of molecules. 12 different assays were used to look for signs of toxicity. The dataset records the results of all 12 assays, each as a different task. Let's also take a look at the weights array. train_dataset. w | deepchem.pdf |
array([[1. 0433141624730409, 1. 0369942196531792, 8. 53921568627451, ..., 1. 060388945752303, 1. 1895710249165168, 1. 0700990099009902], [1. 0433141624730409, 1. 0369942196531792, 1. 1326397919375812, ..., 0. 0, 1. 1895710249165168, 1. 0700990099009902], [0. 0, 0. 0, 0. 0, ..., 1. 060388945752303, 0. 0, 0. 0], ..., [0. 0, 0. 0, 0. 0, ..., 0. 0, 0. 0, 0. 0], [1. 0433141624730409, 1. 0369942196531792, 8. 53921568627451, ..., 1. 060388945752303, 0. 0, 0. 0], [1. 0433141624730409, 1. 0369942196531792, 1. 1326397919375812, ..., 1. 060388945752303, 1. 1895710249165168, 1. 0700990099009902]], dtype=object) Notice that some elements are 0. The weights are being used to indicate missing data. Not all assays were actually performed on every molecule. Setting the weight for a sample or sample/task pair to 0 causes it to be ignored during fitting and evaluation. It will have no effect on the loss function or other metrics. Most of the other weights are close to 1, but not exactly 1. This is done to balance the overall weight of positive and negative samples on each task. When training the model, we want each of the 12 tasks to contribute equally, and on each task we want to put equal weight on positive and negative samples. Otherwise, the model might just learn that most of the training samples are non-toxic, and therefore become biased toward identifying other molecules as non-toxic. Training a Model on Fingerprints Let's train a model. In earlier tutorials we use Graph Conv Model, which is a fairly complicated architecture that takes a complex set of inputs. Because fingerprints are so simple, just a single fixed length array, we can use a much simpler type of model. model = dc. models. Multitask Classifier ( n_tasks = 12, n_features = 1024, layer_sizes = [ 1000 ]) Multitask Classifier is a simple stack of fully connected layers. In this example we tell it to use a single hidden layer of width 1000. We also tell it that each input will have 1024 features, and that it should produce predictions for 12 different tasks. Why not train a separate model for each task? We could do that, but it turns out that training a single model for multiple tasks often works better. We will see an example of that in a later tutorial. Let's train and evaluate the model. import numpy as np model. fit ( train_dataset, nb_epoch = 10 ) metric = dc. metrics. Metric ( dc. metrics. roc_auc_score ) print ( 'training set score:', model. evaluate ( train_dataset, [ metric ], transformers )) print ( 'test set score:', model. evaluate ( test_dataset, [ metric ], transformers )) training set score: {'roc_auc_score': 0. 9550063590563469} test set score: {'roc_auc_score': 0. 7781819573695475} Not bad performance for such a simple model and featurization. More sophisticated models do slightly better on this dataset, but not enormously better. Congratulations! Time to join the Community! Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with Deep Chem, we encourage you to finish the rest of the tutorials in this series. You can also help the Deep Chem community in the following ways: Star Deep Chem on Git Hub This helps build awareness of the Deep Chem project and the tools for open source drug discovery that we're trying to build. Join the Deep Chem Gitter The Deep Chem Gitter hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation! | deepchem.pdf |
Citing This Tutorial If you found this tutorial useful please consider citing it using the provided Bib Te X. @manual { Intro4, title = { Molecular Fingerprints }, organization = { Deep Chem }, author = { Ramsundar, Bharath }, howpublished = { \ url { https : // github. com / deepchem / deepchem / blob / master / examples / tutorials / Molecular_Fingerprints. ipynb year = { 2021 }, } | deepchem.pdf |
Going Deeper On Molecular Featurizations One of the most important steps of doing machine learning on molecular data is transforming the data into a form amenable to the application of learning algorithms. This process is broadly called "featurization" and involves turning a molecule into a vector or tensor of some sort. There are a number of different ways of doing that, and the choice of featurization is often dependent on the problem at hand. We have already seen two such methods: molecular fingerprints, and Conv Mol objects for use with graph convolutions. In this tutorial we will look at some of the others. Colab This tutorial and the rest in this sequence can be done in Google colab. If you'd like to open this notebook in colab, you can use the following link. O p e n i n C o l a b O p e n i n C o l a b ! pip install --pre deepchem import deepchem deepchem. __version__ Featurizers In Deep Chem, a method of featurizing a molecule (or any other sort of input) is defined by a Featurizer object. There are three different ways of using featurizers. 1. When using the Molecule Net loader functions, you simply pass the name of the featurization method to use. We have seen examples of this in earlier tutorials, such as featurizer='ECFP' or featurizer='Graph Conv'. 2. You also can create a Featurizer and directly apply it to molecules. For example: import deepchem as dc featurizer = dc. feat. Circular Fingerprint () print ( featurizer ([ 'CC', 'CCC', 'CCO' ])) [[0. 0. 0. ... 0. 0. 0. ] [0. 0. 0. ... 0. 0. 0. ] [0. 0. 0. ... 0. 0. 0. ]] 3. When creating a new dataset with the Data Loader framework, you can specify a Featurizer to use for processing the data. We will see this in a future tutorial. We use propane (CH 3 CH 2 CH 3, represented by the SMILES string 'CCC' ) as a running example throughout this tutorial. Many of the featurization methods use conformers of the molecules. A conformer can be generated using the Conformer Generator class in deepchem. utils. conformers. RDKit Descriptors RDKit Descriptors featurizes a molecule by using RDKit to compute values for a list of descriptors. These are basic physical and chemical properties: molecular weight, polar surface area, numbers of hydrogen bond donors and acceptors, etc. This is most useful for predicting things that depend on these high level properties rather than on detailed molecular structure. Intrinsic to the featurizer is a set of allowed descriptors, which can be accessed using RDKit Descriptors. allowed Descriptors. The featurizer uses the descriptors in rdkit. Chem. Descriptors. desc List, checks if they are in the list of allowed descriptors, and computes the descriptor value for the molecule. Let's print the values of the first ten descriptors for propane. rdkit_featurizer = dc. feat. RDKit Descriptors () features = rdkit_featurizer ([ 'CCC' ])[ 0 ] for feature, descriptor in zip ( features [: 10 ], rdkit_featurizer. descriptors ): print ( descriptor, feature ) | deepchem.pdf |
Max EState Index 2. 125 Min EState Index 1. 25 Max Abs EState Index 2. 125 Min Abs EState Index 1. 25 qed 0. 3854706587740357 Mol Wt 44. 097 Heavy Atom Mol Wt 36. 033 Exact Mol Wt 44. 062600255999996 Num Valence Electrons 20. 0 Num Radical Electrons 0. 0 Of course, there are many more descriptors than this. print ( 'The number of descriptors present is: ', len ( features )) The number of descriptors present is: 200 Weave Featurizer and Mol Graph Conv Featurizer We previously looked at graph convolutions, which use Conv Mol Featurizer to convert molecules into Conv Mol objects. Graph convolutions are a special case of a large class of architectures that represent molecules as graphs. They work in similar ways but vary in the details. For example, they may associate data vectors with the atoms, the bonds connecting them, or both. They may use a variety of techniques to calculate new data vectors from those in the previous layer, and a variety of techniques to compute molecule level properties at the end. Deep Chem supports lots of different graph based models. Some of them require molecules to be featurized in slightly different ways. Because of this, there are two other featurizers called Weave Featurizer and Mol Graph Conv Featurizer. They each convert molecules into a different type of Python object that is used by particular models. When using any graph based model, just check the documentation to see what featurizer you need to use with it. Coulomb Matrix All the models we have looked at so far consider only the intrinsic properties of a molecule: the list of atoms that compose it and the bonds connecting them. When working with flexible molecules, you may also want to consider the different conformations the molecule can take on. For example, when a drug molecule binds to a protein, the strength of the binding depends on specific interactions between pairs of atoms. To predict binding strength, you probably want to consider a variety of possible conformations and use a model that takes them into account when making predictions. The Coulomb matrix is one popular featurization for molecular conformations. Recall that the electrostatic Coulomb interaction between two charges is proportional to where and are the charges and is the distance between them. For a molecule with atoms, the Coulomb matrix is a matrix where each element gives the strength of the electrostatic interaction between two atoms. It contains information both about the charges on the atoms and the distances between them. More information on the functional forms used can be found here. To apply this featurizer, we first need a set of conformations for the molecule. We can use the Conformer Generator class to do this. It takes a RDKit molecule, generates a set of energy minimized conformers, and prunes the set to only include ones that are significantly different from each other. Let's try running it for propane. from rdkit import Chem generator = dc. utils. Conformer Generator ( max_conformers = 5 ) propane_mol = generator. generate_conformers ( Chem. Mol From Smiles ( 'CCC' )) print ( "Number of available conformers for propane: ", len ( propane_mol. Get Conformers ())) Number of available conformers for propane: 1 It only found a single conformer. This shouldn't be surprising, since propane is a very small molecule with hardly any flexibility. Let's try adding another carbon. butane_mol = generator. generate_conformers ( Chem. Mol From Smiles ( 'CCCC' )) print ( "Number of available conformers for butane: ", len ( butane_mol. Get Conformers ())) | deepchem.pdf |
Number of available conformers for butane: 3 Now we can create a Coulomb matrix for our molecule. coulomb_mat = dc. feat. Coulomb Matrix ( max_atoms = 20 ) features = coulomb_mat ( propane_mol ) print ( features ) | deepchem.pdf |
[[[36. 8581052 12. 48684429 7. 5619687 2. 85945193 2. 85804514 2. 85804556 1. 4674015 1. 46740144 0. 91279491 1. 14239698 1. 14239675 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [12. 48684429 36. 8581052 12. 48684388 1. 46551218 1. 45850736 1. 45850732 2. 85689525 2. 85689538 1. 4655122 1. 4585072 1. 4585072 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [ 7. 5619687 12. 48684388 36. 8581052 0. 9127949 1. 14239695 1. 14239692 1. 46740146 1. 46740145 2. 85945178 2. 85804504 2. 85804493 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [ 2. 85945193 1. 46551218 0. 9127949 0. 5 0. 29325367 0. 29325369 0. 21256978 0. 21256978 0. 12268391 0. 13960187 0. 13960185 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [ 2. 85804514 1. 45850736 1. 14239695 0. 29325367 0. 5 0. 29200271 0. 17113413 0. 21092513 0. 13960186 0. 1680002 0. 20540029 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [ 2. 85804556 1. 45850732 1. 14239692 0. 29325369 0. 29200271 0. 5 0. 21092513 0. 17113413 0. 13960187 0. 20540032 0. 16800016 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [ 1. 4674015 2. 85689525 1. 46740146 0. 21256978 0. 17113413 0. 21092513 0. 5 0. 29351308 0. 21256981 0. 2109251 0. 17113412 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [ 1. 46740144 2. 85689538 1. 46740145 0. 21256978 0. 21092513 0. 17113413 0. 29351308 0. 5 0. 21256977 0. 17113412 0. 21092513 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [ 0. 91279491 1. 4655122 2. 85945178 0. 12268391 0. 13960186 0. 13960187 0. 21256981 0. 21256977 0. 5 0. 29325366 0. 29325365 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [ 1. 14239698 1. 4585072 2. 85804504 0. 13960187 0. 1680002 0. 20540032 0. 2109251 0. 17113412 0. 29325366 0. 5 0. 29200266 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [ 1. 14239675 1. 4585072 2. 85804493 0. 13960185 0. 20540029 0. 16800016 0. 17113412 0. 21092513 0. 29325365 0. 29200266 0. 5 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]]] /Users/peastman/workspace/deepchem/deepchem/feat/molecule_featurizers/coulomb_matrices. py:141: Runtime Warning: d ivide by zero encountered in true_divide m = np. outer(z, z) / d | deepchem.pdf |
Notice that many elements are 0. To combine multiple molecules in a batch we need all the Coulomb matrices to be the same size, even if the molecules have different numbers of atoms. We specified max_atoms=20, so the returned matrix has size (20, 20). The molecule only has 11 atoms, so only an 11 by 11 submatrix is nonzero. Coulomb Matrix Eig An important feature of Coulomb matrices is that they are invariant to molecular rotation and translation, since the interatomic distances and atomic numbers do not change. Respecting symmetries like this makes learning easier. Rotating a molecule does not change its physical properties. If the featurization does change, then the model is forced to learn that rotations are not important, but if the featurization is invariant then the model gets this property automatically. Coulomb matrices are not invariant under another important symmetry: permutations of the atoms' indices. A molecule's physical properties do not depend on which atom we call "atom 1", but the Coulomb matrix does. To deal with this, the Coulumb Matrix Eig featurizer was introduced, which uses the eigenvalue spectrum of the Coulumb matrix and is invariant to random permutations of the atom's indices. The disadvantage of this featurization is that it contains much less information ( eigenvalues instead of an matrix), so models will be more limited in what they can learn. Coulomb Matrix Eig inherits from Coulomb Matrix and featurizes a molecule by first computing the Coulomb matrices for different conformers of the molecule and then computing the eigenvalues for each Coulomb matrix. These eigenvalues are then padded to account for variation in number of atoms across molecules. coulomb_mat_eig = dc. feat. Coulomb Matrix Eig ( max_atoms = 20 ) features = coulomb_mat_eig ( propane_mol ) print ( features ) [[60. 07620303 29. 62963149 22. 75497781 0. 5713786 0. 28781332 0. 28548338 0. 27558187 0. 18163794 0. 17460999 0. 17059719 0. 16640098 0. 0. 0. 0. 0. 0. 0. 0. 0. ]] Congratulations! Time to join the Community! Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with Deep Chem, we encourage you to finish the rest of the tutorials in this series. You can also help the Deep Chem community in the following ways: Star Deep Chem on Git Hub This helps build awareness of the Deep Chem project and the tools for open source drug discovery that we're trying to build. Join the Deep Chem Gitter The Deep Chem Gitter hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation! Citing This Tutorial If you found this tutorial useful please consider citing it using the provided Bib Te X. @manual { Intro7, title = { Going Deeper on Molecular Featurizations }, organization = { Deep Chem }, author = { Ramsundar, Bharath }, howpublished = { \ url { https : // github. com / deepchem / deepchem / blob / master / examples / tutorials / Going_Deeper_on_Molecular_Featurizations year = { 2021 }, } | deepchem.pdf |
Learning Unsupervised Embeddings for Molecules In this tutorial, we will use a Seq To Seq model to generate fingerprints for classifying molecules. This is based on the following paper, although some of the implementation details are different: Xu et al., "Seq2seq Fingerprint: An Unsupervised Deep Molecular Embedding for Drug Discovery" ( https://doi. org/10. 1145/3107411. 3107424 ). Colab This tutorial and the rest in this sequence can be done in Google colab. If you'd like to open this notebook in colab, you can use the following link. O p e n i n C o l a b O p e n i n C o l a b ! pip install --pre deepchem import deepchem deepchem. __version__ Learning Embeddings with Seq To Seq Many types of models require their inputs to have a fixed shape. Since molecules can vary widely in the numbers of atoms and bonds they contain, this makes it hard to apply those models to them. We need a way of generating a fixed length "fingerprint" for each molecule. Various ways of doing this have been designed, such as the Extended-Connectivity Fingerprints (ECFPs) we used in earlier tutorials. But in this example, instead of designing a fingerprint by hand, we will let a Seq To Seq model learn its own method of creating fingerprints. A Seq To Seq model performs sequence to sequence translation. For example, they are often used to translate text from one language to another. It consists of two parts called the "encoder" and "decoder". The encoder is a stack of recurrent layers. The input sequence is fed into it, one token at a time, and it generates a fixed length vector called the "embedding vector". The decoder is another stack of recurrent layers that performs the inverse operation: it takes the embedding vector as input, and generates the output sequence. By training it on appropriately chosen input/output pairs, you can create a model that performs many sorts of transformations. In this case, we will use SMILES strings describing molecules as the input sequences. We will train the model as an autoencoder, so it tries to make the output sequences identical to the input sequences. For that to work, the encoder must create embedding vectors that contain all information from the original sequence. That's exactly what we want in a fingerprint, so perhaps those embedding vectors will then be useful as a way to represent molecules in other models! Let's start by loading the data. We will use the MUV dataset. It includes 74,501 molecules in the training set, and 9313 molecules in the validation set, so it gives us plenty of SMILES strings to work with. import deepchem as dc tasks, datasets, transformers = dc. molnet. load_muv ( split = 'stratified' ) train_dataset, valid_dataset, test_dataset = datasets train_smiles = train_dataset. ids valid_smiles = valid_dataset. ids We need to define the "alphabet" for our Seq To Seq model, the list of all tokens that can appear in sequences. (It's also possible for input and output sequences to have different alphabets, but since we're training it as an autoencoder, they're identical in this case. ) Make a list of every character that appears in any training sequence. tokens = set () for s in train_smiles : tokens = tokens. union ( set ( c for c in s )) tokens = sorted ( list ( tokens )) Create the model and define the optimization method to use. In this case, learning works much better if we gradually decrease the learning rate. We use an Exponential Decay to multiply the learning rate by 0. 9 after each epoch. from deepchem. models. optimizers import Adam, Exponential Decay max_length = max ( len ( s ) for s in train_smiles ) batch_size = 100 batches_per_epoch = len ( train_smiles ) / batch_size model = dc. models. Seq To Seq ( tokens, tokens, max_length, encoder_layers = 2, decoder_layers = 2, embedding_dimension = 256, | deepchem.pdf |
model_dir = 'fingerprint', batch_size = batch_size, learning_rate = Exponential Decay ( 0. 001, 0. 9, batches_per_epoch )) Let's train it! The input to fit_sequences() is a generator that produces input/output pairs. On a good GPU, this should take a few hours or less. def generate_sequences ( epochs ): for i in range ( epochs ): for s in train_smiles : yield ( s, s ) model. fit_sequences ( generate_sequences ( 40 )) Let's see how well it works as an autoencoder. We'll run the first 500 molecules from the validation set through it, and see how many of them are exactly reproduced. predicted = model. predict_from_sequences ( valid_smiles [: 500 ]) count = 0 for s, p in zip ( valid_smiles [: 500 ], predicted ): if ''. join ( p ) == s : count += 1 print ( 'reproduced', count, 'of 500 validation SMILES strings' ) reproduced 161 of 500 validation SMILES strings Now we'll trying using the encoder as a way to generate molecular fingerprints. We compute the embedding vectors for all molecules in the training and validation datasets, and create new datasets that have those as their feature vectors. The amount of data is small enough that we can just store everything in memory. import numpy as np train_embeddings = model. predict_embeddings ( train_smiles ) train_embeddings_dataset = dc. data. Numpy Dataset ( train_embeddings, train_dataset. y, train_dataset. w. astype ( np. float32 ), train_dataset. ids ) valid_embeddings = model. predict_embeddings ( valid_smiles ) valid_embeddings_dataset = dc. data. Numpy Dataset ( valid_embeddings, valid_dataset. y, valid_dataset. w. astype ( np. float32 ), valid_dataset. ids ) For classification, we'll use a simple fully connected network with one hidden layer. classifier = dc. models. Multitask Classifier ( n_tasks = len ( tasks ), n_features = 256, layer_sizes = [ 512 ]) classifier. fit ( train_embeddings_dataset, nb_epoch = 10 ) 0. 0014195525646209716 Find out how well it worked. Compute the ROC AUC for the training and validation datasets. metric = dc. metrics. Metric ( dc. metrics. roc_auc_score, np. mean, mode = "classification" ) train_score = classifier. evaluate ( train_embeddings_dataset, [ metric ], transformers ) valid_score = classifier. evaluate ( valid_embeddings_dataset, [ metric ], transformers ) print ( 'Training set ROC AUC:', train_score ) print ( 'Validation set ROC AUC:', valid_score ) Training set ROC AUC: {'mean-roc_auc_score': 0. 9598792603154332} Validation set ROC AUC: {'mean-roc_auc_score': 0. 7251350862464794} Congratulations! Time to join the Community! Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with Deep Chem, we encourage you to finish the rest of the tutorials in this series. You can also help the Deep Chem community in the following ways: Star Deep Chem on Git Hub This helps build awareness of the Deep Chem project and the tools for open source drug discovery that we're trying to build. Join the Deep Chem Gitter | deepchem.pdf |
The Deep Chem Gitter hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation! | deepchem.pdf |
Synthetic Feasibility Synthetic feasibility is a problem when running large scale enumerations. Often molecules that are enumerated are very difficult to make and thus not worth inspection, even if their other chemical properties are good in silico. This tutorial goes through how to train the Sc Score model [1]. The idea of the model is to train on pairs of molecules where one molecule is "more complex" than the other. The neural network then can make scores which attempt to keep this pairwise ordering of molecules. The final result is a model which can give a relative complexity of a molecule. The paper trains on every reaction in reaxys, declaring products more complex than reactions. Since this training set is prohibitively expensive we will instead train on arbitrary molecules declaring one more complex if its SMILES string is longer. In the real world you can use whatever measure of complexity makes sense for the project. In this tutorial, we'll use the Tox21 dataset to train our simple synthetic feasibility model. Colab This tutorial and the rest in this sequence can be done in Google colab. If you'd like to open this notebook in colab, you can use the following link. O p e n i n C o l a b O p e n i n C o l a b ! pip install --pre deepchem import deepchem deepchem. __version__ Make The Datasets Let's begin by loading some molecules to work with. We load Tox21, specifying splitter=None so everything will be returned as a single dataset. import deepchem as dc tasks, datasets, transformers = dc. molnet. load_tox21 ( featurizer = 'Raw', splitter = None ) molecules = datasets [ 0 ]. X Because Sc Score is trained on relative complexities, we want the X tensor in our dataset to have 3 dimensions (sample_id, molecule_id, features). The molecule_id dimension has size 2 because a sample is a pair of molecules. The label is 1 if the first molecule is more complex than the second molecule. The function create_dataset we introduce below pulls random pairs of SMILES strings out of a given list and ranks them according to this complexity measure. In the real world you could use purchase cost, or number of reaction steps required as your complexity score. from rdkit import Chem import random from deepchem. feat import Circular Fingerprint import numpy as np def create_dataset ( fingerprints, smiles_lens, ds_size = 100000 ): """ m1: list of np. Array fingerprints for molecules m2: list of int length of a molecules SMILES string returns: dc. data. Dataset for input into Sc Score Model Dataset. X shape is (sample_id, molecule_id, features) Dataset. y shape is (sample_id,) values is 1 if the 0th index molecule is more complex 0 if the 1st index molecule is more complex """ X, y = [], [] all_data = list ( zip ( fingerprints, smiles_lens )) while len ( y ) < ds_size : | deepchem.pdf |
i1 = random. randrange ( 0, len ( smiles_lens )) i2 = random. randrange ( 0, len ( smiles_lens )) m1 = all_data [ i1 ] m2 = all_data [ i2 ] if m1 [ 1 ] == m2 [ 1 ]: continue if m1 [ 1 ] > m2 [ 1 ]: y. append ( 1. 0 ) else : y. append ( 0. 0 ) X. append ([ m1 [ 0 ], m2 [ 0 ]]) return dc. data. Numpy Dataset ( np. array ( X ), np. expand_dims ( np. array ( y ), axis = 1 )) With our complexity ranker in place we can now construct our dataset. Let's start by randomly splitting the list of molecules into training and test sets. molecule_ds = dc. data. Numpy Dataset ( np. array ( molecules )) splitter = dc. splits. Random Splitter () train_mols, test_mols = splitter. train_test_split ( molecule_ds ) We'll featurize all our molecules with the ECFP fingerprint with chirality (matching the source paper), and will then construct our pairwise dataset using the function defined above. We are using Circular Fingerprint featurizer, and defining parameters such as the fingerprint size n_features, fingerprint radius radius, and whether to consider chirality chiral. The Circular Fingerprint is a popular type of molecular fingerprint that encodes the structural information of molecules. n_features = 1024 featurizer = dc. feat. Circular Fingerprint ( size = n_features, radius = 2, chiral = True ) train_features = featurizer. featurize ( train_mols. X ) train_smiles_len = [ len ( Chem. Mol To Smiles ( x )) for x in train_mols. X ] train_dataset = create_dataset ( train_features, train_smiles_len ) Now that we have our dataset created, let's train a Sc Score Model on this dataset. model = dc. models. Sc Score Model ( n_features = n_features ) model. fit ( train_dataset, nb_epoch = 20 ) 0. 03494557857513428 Model Performance Lets evaluate how well the model does on our holdout molecules. The Sa Scores should track the length of SMILES strings from never before seen molecules. import matplotlib. pyplot as plt % matplotlib inline mol_scores = model. predict_mols ( test_mols. X ) smiles_lengths = [ len ( Chem. Mol To Smiles ( x )) for x in test_mols. X ] Let's now plot the length of the smiles string of the molecule against the Sa Score using matplotlib. plt. figure ( figsize = ( 20, 16 )) plt. scatter ( smiles_lengths, mol_scores ) plt. xlim ( 0, 80 ) plt. xlabel ( "SMILES length" ) plt. ylabel ( "Sc Score" ) plt. show () | deepchem.pdf |
As we can see the model generally tracks SMILES length. It has good enrichment between 8 and 30 characters and gets both small and large SMILES strings extremes dead on. Now you can train your own models on more meaningful metrics than SMILES length! Congratulations! Time to join the Community! Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with Deep Chem, we encourage you to finish the rest of the tutorials in this series. You can also help the Deep Chem community in the following ways: Star Deep Chem on Git Hub This helps build awareness of the Deep Chem project and the tools for open source drug discovery that we're trying to build. Join the Deep Chem Discord The Deep Chem Discord hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation! Bibliography: [1] https://pubs. acs. org/doi/abs/10. 1021/acs. jcim. 7b00622 | deepchem.pdf |
Calculating Atomic Contributions for Molecules Based on a Graph Convolutional QSAR Model In an earlier tutorial we introduced the concept of model interpretability: understanding why a model produced the result it did. In this tutorial we will learn about atomic contributions, a useful tool for interpreting models that operate on molecules. The idea is simple: remove a single atom from the molecule and see how the model's prediction changes. The "atomic contribution" for an atom is defined as the difference in activity between the whole molecule, and the fragment remaining after atom removal. It is a measure of how much that atom affects the prediction. Contributions are also known as "attributions", "coloration", etc. in the literature. This is a model interpretation method [1], analogous to Similarity maps [2] in the QSAR domain, or occlusion methods in other fields (image classification, etc). Present implementation was used in [4]. Mariia Matveieva, Pavel Polishchuk. Institute of Molecular and Translational Medicine, Palacky University, Olomouc, Czech Republic. Colab This tutorial and the rest in this sequence can be done in Google colab. If you'd like to open this notebook in colab, you can use the following link. O p e n i n C o l a b O p e n i n C o l a b Setup To run Deep Chem within Colab, you'll need to run the following installation commands. This will take about 5 minutes to run to completion and install your environment. You can of course run this tutorial locally if you prefer. In that case, don't run these cells since they will download and install Anaconda on your local machine. ! curl -Lo conda_installer. py https://raw. githubusercontent. com/deepchem/deepchem/master/scripts/colab_install. py import conda_installer conda_installer. install () ! /root/miniconda/bin/conda info -e % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 3457 100 3457 0 0 24692 0 --:--:-- --:--:-- --:--:-- 24692 add /root/miniconda/lib/python3. 7/site-packages to PYTHONPATH python version: 3. 7. 12 fetching installer from https://repo. continuum. io/miniconda/Miniconda3-latest-Linux-x86_64. sh done installing miniconda to /root/miniconda done installing openmm, pdbfixer added conda-forge to channels done conda packages installation finished! # conda environments: # base * /root/miniconda | deepchem.pdf |
! pip install --pre deepchem import deepchem deepchem. __version__ | deepchem.pdf |
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A classification QSAR model for blood-brain barrier permeability BBB permeability is the ability of compounds to enter the central nervous system. Here we use a dataset of relatively small compounds which are transported by diffusion without any carriers. The property is defined as log10(concentration in brain / concentration in blood). Compounds with a positive value (and 0) are labeled active, and others are labeled inactive. After modelling we will identify atoms favorable and unfavorable for diffusion. First let's create the dataset. The molecules are stored in an SDF file. import os import pandas as pd import deepchem as dc import numpy as np from rdkit import Chem from rdkit. Chem import All Chem from rdkit. Chem import Draw, Py Mol, rd FMCS from rdkit. Chem. Draw import IPython Console from rdkit import rd Base from deepchem import metrics from IPython. display import Image, display from rdkit. Chem. Draw import Similarity Maps import tensorflow as tf current_dir = os. path. dirname ( os. path. realpath ( '__file__' )) dc. utils. download_url ( 'https://raw. githubusercontent. com/deepchem/deepchem/master/examples/tutorials/assets/atomic_contributions_tutorial_data/log BB. sdf' current_dir, 'log BB. sdf' ) DATASET_FILE = os. path. join ( current_dir, 'log BB. sdf' ) # Create RDKit mol objects, since we will need them later. mols = [ m for m in Chem. SDMol Supplier ( DATASET_FILE ) if m is not None ] loader = dc. data. SDFLoader ( tasks = [ "log BB_class" ], featurizer = dc. feat. Conv Mol Featurizer (), sanitize = True ) dataset = loader. create_dataset ( DATASET_FILE, shard_size = 2000 ) Now let's build and train a Graph Conv Model. np. random. seed ( 2020 ) tf. random. set_seed ( 2020 ) m = dc. models. Graph Conv Model ( 1, mode = "classification", batch_normalize = False, batch_size = 100 ) m. fit ( dataset, nb_epoch = 10 ) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool_1/ Reshape_14:0", shape=(331,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_po ol_1/Reshape_13:0", shape=(331, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras _model/graph_pool_1/Cast_4:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. 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" % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_conv_1/ Reshape_11:0", shape=(331,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_co nv_1/Reshape_10:0", shape=(331, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras _model/graph_conv_1/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a l arge amount of memory. "shape. This may consume a large amount of memory. " % value) | deepchem.pdf |
/usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_conv_1/ Reshape_13:0", shape=(1646,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_c onv_1/Reshape_12:0", shape=(1646, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker as_model/graph_conv_1/Cast_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. 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" % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_conv_1/ Reshape_27:0", shape=(0,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_conv _1/Reshape_26:0", shape=(0, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras_mod el/graph_conv_1/Cast_8:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a lar ge amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_conv_1/ Reshape_29:0", shape=(0,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_conv _1/Reshape_28:0", shape=(0, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras_mod el/graph_conv_1/Cast_9:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a lar ge amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool/Re shape_14:0", shape=(331,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool /Reshape_13:0", shape=(331, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras_mod el/graph_pool/Cast_4:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool/Re shape_17:0", shape=(1646,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_poo l/Reshape_16:0", shape=(1646, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras_m odel/graph_pool/Cast_5:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a lar ge amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool/Re shape_20:0", shape=(1359,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_poo l/Reshape_19:0", shape=(1359, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras_m odel/graph_pool/Cast_6:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a lar ge amount of memory. | deepchem.pdf |
"shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool/Re shape_23:0", shape=(148,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool /Reshape_22:0", shape=(148, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras_mod el/graph_pool/Cast_7:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool_1/ Reshape_14:0", shape=(334,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_po ol_1/Reshape_13:0", shape=(334, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras _model/graph_pool_1/Cast_4:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool_1/ Reshape_17:0", shape=(1838,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_p ool_1/Reshape_16:0", shape=(1838, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker as_model/graph_pool_1/Cast_5:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool_1/ Reshape_20:0", shape=(1458,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_p ool_1/Reshape_19:0", shape=(1458, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker as_model/graph_pool_1/Cast_6:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool_1/ Reshape_23:0", shape=(120,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_po ol_1/Reshape_22:0", shape=(120, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras _model/graph_pool_1/Cast_7:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_conv_1/ Reshape_11:0", shape=(334,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_co nv_1/Reshape_10:0", shape=(334, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras _model/graph_conv_1/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a l arge amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_conv_1/ Reshape_13:0", shape=(1838,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_c onv_1/Reshape_12:0", shape=(1838, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker as_model/graph_conv_1/Cast_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_conv_1/ Reshape_15:0", shape=(1458,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_c onv_1/Reshape_14:0", shape=(1458, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker as_model/graph_conv_1/Cast_2:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_conv_1/ Reshape_17:0", shape=(120,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_co nv_1/Reshape_16:0", shape=(120, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras _model/graph_conv_1/Cast_3:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool/Re shape_14:0", shape=(334,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool /Reshape_13:0", shape=(334, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras_mod el/graph_pool/Cast_4:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool/Re shape_17:0", shape=(1838,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_poo l/Reshape_16:0", shape=(1838, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras_m odel/graph_pool/Cast_5:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a lar ge amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool/Re shape_20:0", shape=(1458,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_poo l/Reshape_19:0", shape=(1458, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras_m odel/graph_pool/Cast_6:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a lar | deepchem.pdf |
ge amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool/Re shape_23:0", shape=(120,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool /Reshape_22:0", shape=(120, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras_mod el/graph_pool/Cast_7:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool_1/ Reshape_14:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_p ool_1/Reshape_13:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker as_model/graph_pool_1/Cast_4:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool_1/ Reshape_17:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_p ool_1/Reshape_16:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker as_model/graph_pool_1/Cast_5:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool_1/ Reshape_20:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_p ool_1/Reshape_19:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker as_model/graph_pool_1/Cast_6:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_conv_1/ Reshape_11:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_c onv_1/Reshape_10:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker as_model/graph_conv_1/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_conv_1/ Reshape_13:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_c onv_1/Reshape_12:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker as_model/graph_conv_1/Cast_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_conv_1/ Reshape_15:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_c onv_1/Reshape_14:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker as_model/graph_conv_1/Cast_2:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool/Re shape_14:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_poo l/Reshape_13:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras_m odel/graph_pool/Cast_4:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a lar ge amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool/Re shape_17:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_poo l/Reshape_16:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras_m odel/graph_pool/Cast_5:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a lar ge amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool/Re shape_20:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_poo l/Reshape_19:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras_m odel/graph_pool/Cast_6:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a lar ge amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool_1/ Reshape_23:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_p ool_1/Reshape_22:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker as_model/graph_pool_1/Cast_7:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_conv_1/ Reshape_17:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_c onv_1/Reshape_16:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker | deepchem.pdf |
as_model/graph_conv_1/Cast_3:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model/graph_pool/Re shape_23:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model/graph_poo l/Reshape_22:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras_m odel/graph_pool/Cast_7:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a lar ge amount of memory. "shape. This may consume a large amount of memory. " % value) 0. 5348201115926107 Let's load a test set and see how well it works. current_dir = os. path. dirname ( os. path. realpath ( '__file__' )) dc. utils. download_url ( 'https://raw. githubusercontent. com/deepchem/deepchem/master/examples/tutorials/assets/atomic_contributions_tutorial_data/log BB_test_. sdf' current_dir, 'log BB_test_. sdf' ) TEST_DATASET_FILE = os. path. join ( current_dir, 'log BB_test_. sdf' ) loader = dc. data. SDFLoader ( tasks = [ "p_np" ], sanitize = True, featurizer = dc. feat. Conv Mol Featurizer ()) test_dataset = loader. create_dataset ( TEST_DATASET_FILE, shard_size = 2000 ) pred = m. predict ( test_dataset ) pred = np. argmax ( np. squeeze ( pred ), axis = 1 ) ba = metrics. balanced_accuracy_score ( y_true = test_dataset. y, y_pred = pred ) print ( ba ) 0. 7444444444444445 The balanced accuracy is high enough. Now let's proceed to model interpretation and estimate the contributions of individual atoms to the prediction. A fragment dataset Now let's prepare a dataset of fragments based on the training set. (Any other unseen data set of interest can also be used). These fragments will be used to evaluate the contributions of individual atoms. For each molecule we will generate a list of Conv Mol objects. Specifying per_atom_fragmentation=True tells it to iterate over all heavy atoms and featurize a single-atom-depleted version of the molecule with each one removed. loader = dc. data. SDFLoader ( tasks = [], # dont need task (moreover, passing the task can lead to inconsitencies in data shapes) featurizer = dc. feat. Conv Mol Featurizer ( per_atom_fragmentation = True ), sanitize = True ) frag_dataset = loader. create_dataset ( DATASET_FILE, shard_size = 5000 ) /usr/local/lib/python3. 7/dist-packages/numpy/core/_asarray. py:83: Visible Deprecation Warning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray return array(a, dtype, copy=False, order=order) /usr/local/lib/python3. 7/dist-packages/deepchem/data/data_loader. py:885: Visible Deprecation Warning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different len gths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarra y return np. array(features), valid_inds The dataset still has the same number of samples as the original training set, but each sample is now represented as a list of Conv Mol objects (one for each fragment) rather than a single Conv Mol. IMPORTANT: The order of fragments depends on the input format. If SDF, the fragment order is the same as the atom order in corresponding mol blocks. If SMILES (i. e. csv with molecules represented as SMILES), then the order is given by RDKit Canonical Rank Atoms print ( frag_dataset. X. shape ) (298,) We really want to treat each fragment as a separate sample. We can use a Flattening Transformer to flatten the fragments lists. tr = dc. trans. Flattening Transformer ( frag_dataset ) frag_dataset = tr. transform ( frag_dataset ) print ( frag_dataset. X. shape ) (5111,) Predicting atomic contributions to activity | deepchem.pdf |
Now we will predict the activity for molecules and for their fragments. Then, for each fragment, we'll find the activity difference: the change in activity when removing one atom. Note: Here, in classification context, we use the probability output of the model as the activity. So the contribution is the probability difference, i. e. "how much a given atom increases/decreases the probability of the molecule being active. " # whole molecules pred = np. squeeze ( m. predict ( dataset ))[:, 1 ] # probabilitiy of class 1 pred = pd. Data Frame ( pred, index = dataset. ids, columns = [ "Molecule" ]) # turn to dataframe for convinience # fragments pred_frags = np. squeeze ( m. predict ( frag_dataset ))[:, 1 ] pred_frags = pd. Data Frame ( pred_frags, index = frag_dataset. ids, columns = [ "Fragment" ]) We take the difference to find the atomic contributions. # merge 2 dataframes by molecule names df = pd. merge ( pred_frags, pred, right_index = True, left_index = True ) # find contribs df [ 'Contrib' ] = df [ "Molecule" ] - df [ "Fragment" ] df Fragment Molecule Contrib C#CC1(O)CCC2C3C(C)CC4=C(CCC(=O)C4)C3CCC21C 0. 756537 0. 811550 0. 055013 C#CC1(O)CCC2C3C(C)CC4=C(CCC(=O)C4)C3CCC21C 0. 752759 0. 811550 0. 058791 C#CC1(O)CCC2C3C(C)CC4=C(CCC(=O)C4)C3CCC21C 0. 747012 0. 811550 0. 064538 C#CC1(O)CCC2C3C(C)CC4=C(CCC(=O)C4)C3CCC21C 0. 815878 0. 811550-0. 004328 C#CC1(O)CCC2C3C(C)CC4=C(CCC(=O)C4)C3CCC21C 0. 741805 0. 811550 0. 069745............ c1cncc(C2CCCN2)c1 0. 780473 0. 813031 0. 032559 c1cncc(C2CCCN2)c1 0. 722649 0. 813031 0. 090383 c1cncc(C2CCCN2)c1 0. 721607 0. 813031 0. 091425 c1cncc(C2CCCN2)c1 0. 683299 0. 813031 0. 129732 c1cncc(C2CCCN2)c1 0. 674451 0. 813031 0. 138581 5111 rows × 3 columns We can use the Similarity Maps feature of RDKit to visualize the results. Each atom is colored by how it affects activity. def vis_contribs ( mols, df, smi_or_sdf = "sdf" ): # input format of file, which was used to create dataset determines the order of atoms, # so we take it into account for correct mapping! maps = [] for mol in mols : wt = {} if smi_or_sdf == "smi" : for n, atom in enumerate ( Chem. rdmolfiles. Canonical Rank Atoms ( mol )): wt [ atom ] = df. loc [ mol. Get Prop ( "_Name" ), "Contrib" ][ n ] if smi_or_sdf == "sdf" : for n, atom in enumerate ( range ( mol. Get Num Heavy Atoms ())): wt [ atom ] = df. loc [ Chem. Mol To Smiles ( mol ), "Contrib" ][ n ] maps. append ( Similarity Maps. Get Similarity Map From Weights ( mol, wt )) return maps Let's look at some pictures: np. random. seed ( 2000 ) maps = vis_contribs ( np. random. choice ( np. array ( mols ), 10 ), df ) | deepchem.pdf |
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We can see that aromatics or aliphatics have a positive impact on blood-brain barrier permeability, while polar or charged heteroatoms have a negative influence. This is generally consistent with literature data. A regression task The example above used a classification model. The same techniques can also be used for regression models. Let's look at a regression task, aquatic toxicity (towards the water organism T. pyriformis). Toxicity is defined as log10(IGC50) (concentration that inhibits colony growth by 50%). Toxicophores for T. pyriformis will be identified by atomic contributions. All the above steps are the same: load data, featurize, build a model, create dataset of fragments, find contributions, and visualize them. Note: this time as it is regression, contributions will be in activity units, not probability. current_dir = os. path. dirname ( os. path. realpath ( '__file__' )) dc. utils. download_url ( 'https://raw. githubusercontent. com/deepchem/deepchem/master/examples/tutorials/assets/atomic_contributions_tutorial_data/Tetrahymena_pyriformis_Work_set_OCHEM. sdf' current_dir, 'Tetrahymena_pyriformis_Work_set_OCHEM. sdf' ) DATASET_FILE = os. path. join ( current_dir, 'Tetrahymena_pyriformis_Work_set_OCHEM. sdf' ) # create RDKit mol objects, we will need them later mols = [ m for m in Chem. SDMol Supplier ( DATASET_FILE ) if m is not None ] loader = dc. data. SDFLoader ( tasks = [ "IGC50" ], featurizer = dc. feat. Conv Mol Featurizer (), sanitize = True ) dataset = loader. create_dataset ( DATASET_FILE, shard_size = 5000 ) Create and train the model. np. random. seed ( 2020 ) tf. random. set_seed ( 2020 ) m = dc. models. Graph Conv Model ( 1, mode = "regression", batch_normalize = False ) m. fit ( dataset, nb_epoch = 40 ) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 3/Reshape_14:0", shape=(291,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/grap h_pool_3/Reshape_13:0", shape=(291, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_k eras_model_1/graph_pool_3/Cast_4:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may con sume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 3/Reshape_17:0", shape=(910,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/grap h_pool_3/Reshape_16:0", shape=(910, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_k eras_model_1/graph_pool_3/Cast_5:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may con sume a large amount of memory. "shape. This may consume a large amount of memory. " % value) | deepchem.pdf |
/usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 3/Reshape_20:0", shape=(663,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/grap h_pool_3/Reshape_19:0", shape=(663, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_k eras_model_1/graph_pool_3/Cast_6:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may con sume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 3/Reshape_23:0", shape=(28,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph _pool_3/Reshape_22:0", shape=(28, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker as_model_1/graph_pool_3/Cast_7:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consu me a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_conv_ 3/Reshape_11:0", shape=(291,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/grap h_conv_3/Reshape_10:0", shape=(291, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_k eras_model_1/graph_conv_3/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consu me a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_conv_ 3/Reshape_13:0", shape=(910,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/grap h_conv_3/Reshape_12:0", shape=(910, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_k eras_model_1/graph_conv_3/Cast_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may con sume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_conv_ 3/Reshape_15:0", shape=(663,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/grap h_conv_3/Reshape_14:0", shape=(663, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_k eras_model_1/graph_conv_3/Cast_2:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may con sume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_conv_ 3/Reshape_17:0", shape=(28,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph _conv_3/Reshape_16:0", shape=(28, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker as_model_1/graph_conv_3/Cast_3:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consu me a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_conv_ 3/Reshape_19:0", shape=(0,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_ conv_3/Reshape_18:0", shape=(0, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras _model_1/graph_conv_3/Cast_4:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_conv_ 3/Reshape_21:0", shape=(0,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_ conv_3/Reshape_20:0", shape=(0, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras _model_1/graph_conv_3/Cast_5:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_conv_ 3/Reshape_23:0", shape=(0,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_ conv_3/Reshape_22:0", shape=(0, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras _model_1/graph_conv_3/Cast_6:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_conv_ 3/Reshape_25:0", shape=(0,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_ conv_3/Reshape_24:0", shape=(0, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras _model_1/graph_conv_3/Cast_7:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_conv_ 3/Reshape_27:0", shape=(0,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_ conv_3/Reshape_26:0", shape=(0, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras _model_1/graph_conv_3/Cast_8:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_conv_ 3/Reshape_29:0", shape=(0,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_ conv_3/Reshape_28:0", shape=(0, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_keras _model_1/graph_conv_3/Cast_9:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory. | deepchem.pdf |
"shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 2/Reshape_14:0", shape=(291,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/grap h_pool_2/Reshape_13:0", shape=(291, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_k eras_model_1/graph_pool_2/Cast_4:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may con sume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 2/Reshape_17:0", shape=(910,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/grap h_pool_2/Reshape_16:0", shape=(910, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_k eras_model_1/graph_pool_2/Cast_5:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may con sume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 2/Reshape_20:0", shape=(663,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/grap h_pool_2/Reshape_19:0", shape=(663, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_k eras_model_1/graph_pool_2/Cast_6:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may con sume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 2/Reshape_23:0", shape=(28,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph _pool_2/Reshape_22:0", shape=(28, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker as_model_1/graph_pool_2/Cast_7:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consu me a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 3/Reshape_14:0", shape=(307,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/grap h_pool_3/Reshape_13:0", shape=(307, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_k eras_model_1/graph_pool_3/Cast_4:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may con sume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 3/Reshape_17:0", shape=(944,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/grap h_pool_3/Reshape_16:0", shape=(944, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_k eras_model_1/graph_pool_3/Cast_5:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may con sume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 3/Reshape_20:0", shape=(693,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/grap h_pool_3/Reshape_19:0", shape=(693, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_k eras_model_1/graph_pool_3/Cast_6:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may con sume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 3/Reshape_23:0", shape=(16,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph _pool_3/Reshape_22:0", shape=(16, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker as_model_1/graph_pool_3/Cast_7:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consu me a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_conv_ 3/Reshape_11:0", shape=(307,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/grap h_conv_3/Reshape_10:0", shape=(307, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_k eras_model_1/graph_conv_3/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consu me a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_conv_ 3/Reshape_13:0", shape=(944,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/grap h_conv_3/Reshape_12:0", shape=(944, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_k eras_model_1/graph_conv_3/Cast_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may con sume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_conv_ 3/Reshape_15:0", shape=(693,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/grap h_conv_3/Reshape_14:0", shape=(693, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_k eras_model_1/graph_conv_3/Cast_2:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may con sume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_conv_ 3/Reshape_17:0", shape=(16,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph _conv_3/Reshape_16:0", shape=(16, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker as_model_1/graph_conv_3/Cast_3:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consu | deepchem.pdf |
me a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 2/Reshape_14:0", shape=(307,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/grap h_pool_2/Reshape_13:0", shape=(307, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_k eras_model_1/graph_pool_2/Cast_4:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may con sume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 2/Reshape_17:0", shape=(944,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/grap h_pool_2/Reshape_16:0", shape=(944, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_k eras_model_1/graph_pool_2/Cast_5:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may con sume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 2/Reshape_20:0", shape=(693,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/grap h_pool_2/Reshape_19:0", shape=(693, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_k eras_model_1/graph_pool_2/Cast_6:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may con sume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 2/Reshape_23:0", shape=(16,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph _pool_2/Reshape_22:0", shape=(16, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv_ker as_model_1/graph_pool_2/Cast_7:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consu me a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 3/Reshape_14:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/gra ph_pool_3/Reshape_13:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv _keras_model_1/graph_pool_3/Cast_4:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may c onsume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 3/Reshape_17:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/gra ph_pool_3/Reshape_16:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv _keras_model_1/graph_pool_3/Cast_5:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may c onsume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 3/Reshape_20:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/gra ph_pool_3/Reshape_19:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv _keras_model_1/graph_pool_3/Cast_6:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may c onsume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 3/Reshape_23:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/gra ph_pool_3/Reshape_22:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv _keras_model_1/graph_pool_3/Cast_7:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may c onsume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_conv_ 3/Reshape_11:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/gra ph_conv_3/Reshape_10:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv _keras_model_1/graph_conv_3/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may con sume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_conv_ 3/Reshape_13:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/gra ph_conv_3/Reshape_12:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv _keras_model_1/graph_conv_3/Cast_1:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may c onsume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_conv_ 3/Reshape_15:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/gra ph_conv_3/Reshape_14:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv _keras_model_1/graph_conv_3/Cast_2:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may c onsume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_conv_ 3/Reshape_17:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/gra ph_conv_3/Reshape_16:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv | deepchem.pdf |
_keras_model_1/graph_conv_3/Cast_3:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may c onsume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 2/Reshape_14:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/gra ph_pool_2/Reshape_13:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv _keras_model_1/graph_pool_2/Cast_4:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may c onsume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 2/Reshape_17:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/gra ph_pool_2/Reshape_16:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv _keras_model_1/graph_pool_2/Cast_5:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may c onsume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 2/Reshape_20:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/gra ph_pool_2/Reshape_19:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv _keras_model_1/graph_pool_2/Cast_6:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may c onsume a large amount of memory. "shape. This may consume a large amount of memory. " % value) /usr/local/lib/python3. 7/dist-packages/tensorflow/python/framework/indexed_slices. py:450: User Warning: Convertin g sparse Indexed Slices(Indexed Slices(indices=Tensor("gradient_tape/private__graph_conv_keras_model_1/graph_pool_ 2/Reshape_23:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/private__graph_conv_keras_model_1/gra ph_pool_2/Reshape_22:0", shape=(None, 64), dtype=float32), dense_shape=Tensor("gradient_tape/private__graph_conv _keras_model_1/graph_pool_2/Cast_7:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may c onsume a large amount of memory. "shape. This may consume a large amount of memory. " % value) 0. 12407124519348145 Load the test dataset and check the model's performance. current_dir = os. path. dirname ( os. path. realpath ( '__file__' )) dc. utils. download_url ( 'https://raw. githubusercontent. com/deepchem/deepchem/master/examples/tutorials/assets/atomic_contributions_tutorial_data/Tetrahymena_pyriformis_Test_set_OCHEM. sdf' current_dir, 'Tetrahymena_pyriformis_Test_set_OCHEM. sdf' ) TEST_DATASET_FILE = os. path. join ( current_dir, 'Tetrahymena_pyriformis_Test_set_OCHEM. sdf' ) loader = dc. data. SDFLoader ( tasks = [ "IGC50" ], sanitize = True, featurizer = dc. feat. Conv Mol Featurizer ()) test_dataset = loader. create_dataset ( TEST_DATASET_FILE, shard_size = 2000 ) pred = m. predict ( test_dataset ) mse = metrics. mean_squared_error ( y_true = test_dataset. y, y_pred = pred ) r2 = metrics. r2_score ( y_true = test_dataset. y, y_pred = pred ) print ( mse ) print ( r2 ) 0. 2381780323921622 0. 784334539071699 Load the training set again, but this time set per_atom_fragmentation=True. loader = dc. data. SDFLoader ( tasks = [], # dont need any task sanitize = True, featurizer = dc. feat. Conv Mol Featurizer ( per_atom_fragmentation = True )) frag_dataset = loader. create_dataset ( DATASET_FILE, shard_size = 5000 ) tr = dc. trans. Flattening Transformer ( frag_dataset ) # flatten dataset and add ids to each fragment frag_dataset = tr. transform ( frag_dataset ) /usr/local/lib/python3. 7/dist-packages/numpy/core/_asarray. py:83: Visible Deprecation Warning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray return array(a, dtype, copy=False, order=order) /usr/local/lib/python3. 7/dist-packages/deepchem/data/data_loader. py:885: Visible Deprecation Warning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different len gths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarra y return np. array(features), valid_inds Compute the activity differences. # whole molecules pred = m. predict ( dataset ) pred = pd. Data Frame ( pred, index = dataset. ids, columns = [ "Molecule" ]) # turn to dataframe for convenience | deepchem.pdf |
# fragments pred_frags = m. predict ( frag_dataset ) pred_frags = pd. Data Frame ( pred_frags, index = frag_dataset. ids, columns = [ "Fragment" ]) # turn to dataframe for convenience # merge 2 dataframes by molecule names df = pd. merge ( pred_frags, pred, right_index = True, left_index = True ) # find contribs df [ 'Contrib' ] = df [ "Molecule" ] - df [ "Fragment" ] Lets take some molecules with moderate activity (not extremely active/inactive) and visualize the atomic contributions. maps = vis_contribs ([ mol for mol in mols if float ( mol. Get Prop ( "IGC50" )) > 3 and float ( mol. Get Prop ( "IGC50" )) < 4 ][: 10 ], | deepchem.pdf |
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We can see that known toxicophores are in green, namely nitro-aromatics, halo-aromatics, long alkyl chains, and aldehyde; while carboxylic groups, alcohols, and aminos are detoxyfying, as is consistent with literature [3] Appendix In this tutorial we operated on SDF files. However, if we use CSV files with SMILES as input, the order of the atoms in the dataframe DOES NOT correspond to the original atom order. If we want to recover the original atom order for each molecule (to have it in our main dataframe), we need to use RDKit's Chem. rdmolfiles. Canonical Rank Atoms. Here are some utilities to do this. | deepchem.pdf |
We can add a column with atom ids (as in input molecules) and use the resulting dataframe for analysis with any other software, outside the "python-rdkit-deepchem" environment. def get_mapping ( mols, mol_names ): """perform mapping: atom number original <-> atom number(position) after ranking (both 1-based)""" # mols - RDKit mols # names - any seq of strings # return list of nested lists: [[molecule, [atom , atom, ..], [... ]] assert ( len ( mols ) == len ( mol_names )) mapping = [] for m, n in zip ( mols, mol_names ): atom_ids = [ i + 1 for i in list ( Chem. rdmolfiles. Canonical Rank Atoms ( m ))] mapping. append ([ n, atom_ids ]) return mapping def append_atomid_col ( df, mapping ): # add column with CORRECT atom number(position) for i in mapping : df. loc [ i [ 0 ], "Atom ID" ] = i [ 1 ] return df Bibliography: 1. Polishchuk, P., O. Tinkov, T. Khristova, L. Ognichenko, A. Kosinskaya, A. Varnek & V. Kuz'min (2016) Structural and Physico-Chemical Interpretation (SPCI) of QSAR Models and Its Comparison with Matched Molecular Pair Analysis. Journal of Chemical Information and Modeling, 56, 1455-1469. 2. Riniker, S. & G. Landrum (2013) Similarity maps - a visualization strategy for molecular fingerprints and machine-learning methods. Journal of Cheminformatics, 5, 43. 3. M. Matveieva, M. T. D. Cronin, P. Polishchuk, Mol. Inf. 2019, 38, 1800084. 4. Matveieva, M., Polishchuk, P. Benchmarks for interpretation of QSAR models. J Cheminform 13, 41 (2021). https://doi. org/10. 1186/s13321-021-00519-x Congratulations! Time to join the Community! Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with Deep Chem, we encourage you to finish the rest of the tutorials in this series. You can also help the Deep Chem community in the following ways: Star Deep Chem on Git Hub This helps build awareness of the Deep Chem project and the tools for open source drug discovery that we're trying to build. Join the Deep Chem Discord The Deep Chem Discord hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation! | deepchem.pdf |