Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
Department of Pharmaceutical Technology, JIS University, 81, Nilgunj Road, Agarpara, Kolkata, West Bengal, India; Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
J Mol Graph Model. 2023 Sep;123:108510. doi: 10.1016/j.jmgm.2023.108510. Epub 2023 May 16.
Histone deacetylase (HDAC) inhibitors are in the limelight of anticancer drug development and research. HDAC10 is one of the class-IIb HDACs, responsible for cancer progression. The search for potent and effective HDAC10 selective inhibitors is going on. However, the absence of human HDAC10 crystal/NMR structure hampers the structure-based drug design of HDAC10 inhibitors. Different ligand-based modeling techniques are the only hope to speed up the inhibitor design. In this study, we applied different ligand-based modeling techniques on a diverse set of HDAC10 inhibitors (n = 484). Machine learning (ML) models were developed that could be used to screen unknown compounds as HDAC10 inhibitors from a large chemical database. Moreover, Bayesian classification and Recursive partitioning models were used to identify the structural fingerprints regulating the HDAC10 inhibitory activity. Additionally, a molecular docking study was performed to understand the binding pattern of the identified structural fingerprints towards the active site of HDAC10. Overall, the modeling insight might offer helpful information for medicinal chemists to design and develop efficient HDAC10 inhibitors.
组蛋白去乙酰化酶 (HDAC) 抑制剂是抗癌药物开发和研究的焦点。HDAC10 是 IIb 类 HDAC 之一，负责癌症的进展。人们正在寻找强效且有效的 HDAC10 选择性抑制剂。然而，由于缺乏人源 HDAC10 晶体/NMR 结构，阻碍了基于结构的 HDAC10 抑制剂药物设计。不同的基于配体的建模技术是加快抑制剂设计的唯一希望。在这项研究中，我们应用了不同的基于配体的建模技术对一组多样化的 HDAC10 抑制剂（n=484）进行了研究。开发了机器学习 (ML) 模型，可用于从大型化学数据库中筛选未知化合物作为 HDAC10 抑制剂。此外，贝叶斯分类和递归分区模型用于识别调节 HDAC10 抑制活性的结构指纹。此外，还进行了分子对接研究，以了解鉴定出的结构指纹与 HDAC10 活性位点的结合模式。总的来说，该模型研究提供的见解可能为药物化学家设计和开发高效的 HDAC10 抑制剂提供有用的信息。
