Supplementary Information showing a list of all the ligand features considered from A simple spatial extension to the extended connectivity interaction features for binding affinity prediction

The representation of the protein-ligand complexes used in building machine learning models play an important role in the accuracy of binding affinity prediction. The Extended Connectivity Interaction Features (ECIF) is one such representation. We report that (i) including the discretized distances between protein-ligand atom pairs in the ECIF scheme improves predictive accuracy, and (ii) in an evaluation using gradient boosted trees, we found that the resampling method used in selecting the best hyperparameters has a strong effect on predictive performance, especially for benchmarking purposes.

用于构建机器学习模型的蛋白质-配体复合物的表征方式，对结合亲和力预测的准确性具有关键作用。扩展连接交互特征（Extended Connectivity Interaction Features，ECIF）即为这类表征方式之一。本研究揭示两点结论：其一，在ECIF框架中纳入蛋白质-配体原子对的离散化距离，可有效提升预测精度；其二，在基于梯度提升树（gradient boosted trees）的评估实验中，用于筛选最优超参数的重采样方法对预测性能存在显著影响，尤其在基准测试场景中该效应更为突出。