MarkusFerdinandDablander/QSAR-activity-cliff-experiments
Exploring QSAR Models for Activity-Cliff Prediction
This project helps medicinal chemists and computational chemists understand how small changes in molecular structure impact drug activity, a phenomenon known as an 'activity cliff.' It takes clean chemical data sets, including SMILES strings and activity values, to build and evaluate models. The output provides insights into activity cliff predictions, which can guide drug discovery efforts.
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Use this if you are a medicinal chemist or computational chemist interested in exploring and reproducing advanced QSAR models for predicting activity cliffs in drug discovery data.
Not ideal if you are looking for a ready-to-use software application without needing to engage with code or model training.
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23
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7
Language
Jupyter Notebook
License
MIT
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Last pushed
May 17, 2024
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