Machine Learning‐Enabled Virtual Screening with Multiple Protein Structures toward the Discovery of Novel JAK3 Inhibitors: Integration of Molecular Docking, Pharmacophore, and Naïve Bayesian Classification

药效团 虚拟筛选 对接(动物) 计算生物学 Janus激酶3 计算机科学 药物发现 仿形(计算机编程) 分子动力学 机器学习 化学 人工智能 生物信息学 生物 立体化学 计算化学 生物化学 医学 抗原提呈细胞 细胞毒性T细胞 护理部 体外 操作系统
作者
Jingyu Zhu,Jingyu Sun,Lei Jia,Lei Xu,Yanfei Cai,Yun Chen,Jian Jin
出处
期刊:Advanced theory and simulations [Wiley]
卷期号:6 (7) 被引量:2
标识
DOI:10.1002/adts.202200835
摘要

Abstract Extensive 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.
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