  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.

Biomolecules;Machine Learning