Affiliation:
1. School of Life Sciences and Health Engineering Jiangnan University Wuxi Jiangsu 214122 China
2. Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering Jiangsu University of Technology Changzhou 213001 China
Abstract
AbstractExtensive research has accumulated suggesting that Janus kinase 3 (JAK3) is closely related to the occurrence and development of various human diseases, making JAK3 a highly potential drug target. However, JAK3 has high homology with other members of the JAK family, making the development of JAK3 inhibitors full of challenges. Thus, here, a naïve Bayesian classification (NBC) model based on multiple JAK3 protein conformations, which integrates molecular docking, pharmacophore, and molecular descriptors, is developed to find novel JAK3 inhibitors. First, the validation set is used to prove whether molecular docking or pharmacophore, integrating multiple JAK3 conformations always has higher prediction accuracy than that of any single conformation. Second, external prediction reveals that the NBC model combining molecular docking, pharmacophore, and important molecular features could significantly improve the enrichment of active JAK3 inhibitors. Finally, the optimal NBC model is utilized for virtual screening against a large chemical database and some compounds with high Bayesian scores are identified. Altogether, the machine learning‐based virtual screening protocol not only has strong efficiency but also has high screening accuracy. It is hoped that the developed virtual screening strategy could provide valuable guidance for the discovery of novel JAK3 inhibitors.
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Subject
Multidisciplinary,Modeling and Simulation,Numerical Analysis,Statistics and Probability
Cited by
1 articles.
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