ADMET property prediction through combinations of molecular fingerprints

• URL: http://arxiv.org/abs/2310.00174v1
• Date: Fri, 29 Sep 2023 22:39:18 GMT
• Title: ADMET property prediction through combinations of molecular fingerprints
• Authors: James H. Notwell and Michael W. Wood
• Abstract summary: Random forests or support vector machines paired with extended-connectivity fingerprints consistently outperformed recently developed methods. A detailed investigation into regression algorithms and molecular fingerprints revealed gradient-boosted decision trees. We successfully validated our model across 22 Therapeutics Data Commons ADMET benchmarks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While investigating methods to predict small molecule potencies, we found random forests or support vector machines paired with extended-connectivity fingerprints (ECFP) consistently outperformed recently developed methods. A detailed investigation into regression algorithms and molecular fingerprints revealed gradient-boosted decision trees, particularly CatBoost, in conjunction with a combination of ECFP, Avalon, and ErG fingerprints, as well as 200 molecular properties, to be most effective. Incorporating a graph neural network fingerprint further enhanced performance. We successfully validated our model across 22 Therapeutics Data Commons ADMET benchmarks. Our findings underscore the significance of richer molecular representations for accurate property prediction.

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