Bhagat Rinki Prasad, Dasgupta Indrasis, Amin Sk Abdul, Jakkula Pranay, Bhattacharya Arijit, Qureshi Insaf Ahmed, Gayen Shovanlal
Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
Department of Biotechnology & Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, 500046, India.
Mol Divers. 2025 May 17. doi: 10.1007/s11030-025-11217-3.
In the histone deacetylase (HDAC) family, HDAC11 is the smallest and a single member under the class IV subtype. It is important as a drug target mainly in cancer, inflammatory and autoimmune diseases. The design and development of selective HDAC11 inhibitors is quite a challenge for the chemist community due to the unavailability of the crystal structure of HDAC11. Ligand-based drug design (LBDD) strategies are the hope to speed up the development of its inhibitors. Here, an in-depth analysis of 712 HDAC11 inhibitors is performed through compound space networks and various cheminformatics approaches. The analyses demonstrated significant clustering of similar compounds based on their chemical structures, offering valuable insights into the chemical space occupied by HDAC11 inhibitors. Furthermore, the current work aimed to develop robust classification-based QSAR models that deliver the essential structural fingerprints. This study highlighted that the compounds bearing scaffolds such as isoindoline, benzimidazole, carboxamide/hydroxamate moieties, etc., are important for HDAC11 inhibitors. Molecular docking and MD simulations further provide an in-depth analysis of the binding interaction of the identified fingerprints in the catalytic site of HDAC11. In brief, our study delivers some important structural attributes that will aid medicinal chemists in designing and developing future potent HDAC11 inhibitors.
在组蛋白去乙酰化酶(HDAC)家族中,HDAC11是最小的成员,属于IV类亚型中的唯一成员。它作为药物靶点主要在癌症、炎症和自身免疫性疾病中具有重要意义。由于HDAC11晶体结构不可用,设计和开发选择性HDAC11抑制剂对化学界来说是一项相当大的挑战。基于配体的药物设计(LBDD)策略是加速其抑制剂开发的希望所在。在此,通过化合物空间网络和各种化学信息学方法对712种HDAC11抑制剂进行了深入分析。分析表明,基于其化学结构,相似化合物存在显著聚类,这为HDAC11抑制剂所占据的化学空间提供了有价值的见解。此外,当前工作旨在开发强大的基于分类的QSAR模型,以提供基本的结构指纹。这项研究强调,带有异吲哚啉、苯并咪唑、羧酰胺/异羟肟酸酯部分等支架的化合物对HDAC11抑制剂很重要。分子对接和分子动力学模拟进一步深入分析了所确定的指纹在HDAC11催化位点的结合相互作用。简而言之,我们的研究提供了一些重要的结构属性,这将有助于药物化学家设计和开发未来有效的HDAC11抑制剂。