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.

通过使用随机森林或支持向量机等与扩展连通性指纹 (ECFP) 相结合的方法，我们发现这些方法在预测小分子效能方面始终表现优于最近开发的方法。进一步研究回归算法和分子指纹发现，梯度提升决策树（尤其是 CatBoost），结合 ECFP、Avalon 和 ErG 指纹以及 200 种分子属性，最为有效。引入图神经网络指纹进一步提升了性能。我们通过在 22 个药物治疗数据共享平台 (Therapeutics Data Commons) 的 ADMET 基准中成功验证了我们的模型。我们的发现突显了丰富的分子表示对于准确预测属性的重要性。