---
license: apache-2.0
dataset_info:
  features:
  - name: Smiles
    dtype: string
  - name: DockingScore
    dtype: float64
  - name: dG
    dtype: float64
  - name: dGError
    dtype: float64
  splits:
  - name: train
    num_bytes: 641714
    num_examples: 8997
  - name: test
    num_bytes: 71163
    num_examples: 1000
  download_size: 315048
  dataset_size: 712877
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
tags:
- molecule
- chemistry
- smiles
- free_energy
size_categories:
- 1K<n<10K
---

 Molecular dataset: 10,000 TYK2 inhibitors (SMILES strings) with Docking scores and Relative Binding Free Energy (dG) 


Dataset from paper:

James Thompson, W Patrick Walters, Jianwen A Feng, Nicolas A Pabon, Hongcheng Xu, Michael Maser, Brian B Goldman, Demetri Moustakas, Molly Schmidt, Forrest York,
Optimizing active learning for free energy calculations, Artificial Intelligence in the Life Sciences, Volume 2, 2022, 100050, ISSN 2667-3185,
https://doi.org/10.1016/j.ailsci.2022.100050.

https://www.sciencedirect.com/science/article/pii/S2667318522000204

original source: https://github.com/google-research/google-research/tree/master/al_for_fep