Development of Predictive Models for Identifying Potential S100A9 Inhibitors Based on Machine Learning Methods

S100A9 is a potential therapeutic target for various disease including prostate cancer, colorectal cancer, and Alzheimer’s disease. However, the sparsity of atomic level data such as protein-protein interaction of S100A9 with MD2/TLR4/CD147 makes rational drug design of S100A9 inhibitors more challengeable. Herein we firstly report predictive models of S100A9 inhibitory effect by applying machine learning classifiers on 2D-molecular descriptors. The models were optimized through feature selectors as well as classifiers to produce the top eight random forest models with robust predictability as well as high cost-effectiveness. Notably, the optimal feature sets were obtained after the reduction of 2798 features into dozens of features with the chopping of fingerprint bits. In addition, the high efficiency of compact feature sets allowed us to further screen a large-scale dataset (over 6,000,000 compounds) within a week. Through the consensus vote of the top models, 46 hits (hit rate = 0.000713%) were identified as potential S100A9 inhibitors. We expect that our models will facilitate the drug discovery process by providing high predictive power as well as cost-reduction ability and give insights into the design of the novel drugs targeting S100A9.