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# Model documentation & parameters | |
**Algorithm Version**: Which model version to use. | |
**Target binding energy**: The desired binding energy. The optimal range determined in [literature](https://doi.org/10.1039/C8SC01949E) is between -31.1 and -23.0 kcal/mol. | |
**Primer SMILES**: A SMILES string is used to prime the generation. | |
**Maximal sequence length**: The maximal number of tokens in the generated molecule. | |
**Number of points**: Number of points to sample with the Gaussian Process. | |
**Number of steps**: Number of optimization steps in the Gaussian Process optimization. | |
**Number of samples**: How many samples should be generated (between 1 and 50). | |
# Model card -- AdvancedManufacturing | |
**Model Details**: *AdvancedManufacturing* is a sequence-based molecular generator tuned to generate catalysts. The model relies on a recurrent Variational Autoencoder with a binding-energy predictor trained on the latent code. The framework uses Gaussian Processes for generating targeted molecules. | |
**Developers**: Oliver Schilter and colleagues from IBM Research. | |
**Distributors**: Original authors' code integrated into GT4SD. | |
**Model date**: Not yet published. Manuscript accepted. | |
**Model version**: Different types of models trained on 7054 data points are represented either as SMILES or SELFIES. Augmentation was used to broaden the scope augmentation. | |
**Model type**: A sequence-based molecular generator tuned to generate catalysts. The model relies on a recurrent Variational Autoencoder with a binding-energy predictor trained on the latent code. The framework uses Gaussian Processes for generating targeted molecules. | |
**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**: | |
N.A. | |
**Paper or other resources for more information**: | |
**License**: MIT | |
**Where to send questions or comments about the model**: Open an issue on [GT4SD repository](https://github.com/GT4SD/gt4sd-core). | |
**Intended Use. Use cases that were envisioned during development**: Chemical research, in particular, to discover new Suzuki cross-coupling catalysts. | |
**Primary intended uses/users**: Researchers and computational chemists using the model for research exploration purposes. | |
**Out-of-scope use cases**: Production-level inference, producing molecules with harmful properties. | |
**Metrics**: N.A. | |
**Datasets**: Data used for training was provided through the NCCR and can be found [here](https://doi.org/10.24435/materialscloud:2018.0014/v1) and [here](https://doi.org/10.24435/materialscloud:2019.0007/v3). | |
**Ethical Considerations**: Unclear, please consult with original authors in case of questions. | |
**Caveats and Recommendations**: Unclear, please consult with original authors in case of questions. | |
Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs) | |
## Citation | |
Please cite: | |
```bib | |
@article{manica2023accelerating, | |
title={Accelerating material design with the generative toolkit for scientific discovery}, | |
author={Manica, Matteo and Born, Jannis and Cadow, Joris and Christofidellis, Dimitrios and Dave, Ashish and Clarke, Dean and Teukam, Yves Gaetan Nana and Giannone, Giorgio and Hoffman, Samuel C and Buchan, Matthew and others}, | |
journal={npj Computational Materials}, | |
volume={9}, | |
number={1}, | |
pages={69}, | |
year={2023}, | |
publisher={Nature Publishing Group UK London} | |
} | |
``` |