This repo contains associated code for the paper Learning chemical intuition from humans in the loop.
We recommend that you make a fresh conda environment (currently we only support Python 3.8-3.10 Linux builds) and install the provided conda package for convenience:
conda install molskill -c msr-ai4science -c conda-forgeAdditionally, you can also use the provided environment.yml file for manual installation.
A CUDA-enabled GPU is not required for usage, but strongly recommended for speed if you plan on scoring a large amount of compounds.
This work mainly exposes the MolSkillScorer class under the molskill.scorer module. We interface with RDKit to provide predictions accordingly. The user only has to provide a list of molecular strings that they wish to score.
from molskill.scorer import MolSkillScorer
smiles_strs = ["CCO", "O=C(Oc1ccccc1C(=O)O)C"]
scorer = MolSkillScorer()
scores = scorer.score(smiles_strs)We provide and use by default a pre-trained model on all the data that was collected during the original study. If a user wants to train custom models, please check the train.py script also included under this repository.
Note: The default model and featurizer does not support non-organic elements or molecules with multiple fragments. Furthermore we suggest that you pass the NIBR filters on your compounds before running them through default scorer. We recommend doing this to avoid out-of-distribution biases, as the provided models have never seen a violating molecule during training time. The filters are nowadays available on the RDKit - a guide on how to apply those is provided here.
If you find this work or parts thereof useful, please consider citing the following BibTeX entry:
@article{choung2023,
place={Cambridge},
title={Learning chemical intuition from humans in the loop},
DOI={10.26434/chemrxiv-2023-knwnv},
journal={ChemRxiv},
publisher={Cambridge Open Engage},
author={Choung, Oh-Hyeon and Vianello, Riccardo and Segler, Marwin and Stiefl, Nikolaus and Jiménez-Luna, José},
year={2023}}
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