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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Chem. | doi: 10.3389/fchem.2019.00779

Development of predictive models for identifying potential S100A9 inhibitors based on machine learning methods

  • 1College of Pharmacy, Gachon University of Medicine and Science, South Korea
  • 2Neuroscience Research Institute, Gachon University, South Korea

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.

Keywords: S100, machine learning, MRP, random forest, Ligand-based virtual screening, Feature Selection, Classification, Consensus vote, Alzheimer’s disease, Alzheimer’s disease (AD)

Received: 10 Feb 2019; Accepted: 29 Oct 2019.

Copyright: © 2019 Lee, Kumar, Lee, Park and Kim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Mi-hyun Kim, College of Pharmacy, Gachon University of Medicine and Science, Incheon, South Korea, kmh0515@gachon.ac.kr