HDACiAP: A Curated Database and Analytical Platform for Histone Deacetylase Inhibitors

IF 6.4 2区化学 Q1 CHEMISTRY, MEDICINAL

Journal of Chemical Information and Modeling Pub Date : 2026-10-12 Epub Date: 2026-09-21 DOI:10.1021/acs.jcim.6c00572

Yuhe Xiao, Jiaping Ou, Jiatong Chen, Xiaolu Zhou, Xiaoying Fu, Jiaqi Hu, Zhipeng Ye, Guangying Chen, Wenyuan Kang

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引用次数: 0

Abstract

Histone deacetylases (HDACs) are pivotal epigenetic regulators that are aberrantly expressed in various cancers, making them prominent targets for anticancer drug development. Herein, we present HDACiAP (https://hdac.kangsgo.cn), a meticulously curated, comprehensive database and analytical platform dedicated to HDAC inhibitors. HDACiAP currently encompasses 32,721 compounds with 123,154 bioactivity records, integrating multidimensional data such as physicochemical properties, biological activities, toxicity predictions, compound selectivity annotations, computationally generated docking poses, and protein–ligand interaction profiles. The platform incorporates built-in molecular docking and integrated machine learning and deep learning-based prediction modules, enabling users to evaluate the potency of novel compounds. Additionally, HDACiAP offers an intuitive visualization interface, multimodal search capabilities, and programmatic access via RESTful APIs. In summary, by providing a curated bioactivity resource together with computational docking annotations and predictive models, HDACiAP offers a practical platform for exploring HDAC inhibitor chemical space, supporting preliminary virtual screening, and facilitating AI-assisted discovery of HDAC-targeted compounds.

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HDACiAP：组蛋白去乙酰化酶抑制剂的数据库和分析平台

组蛋白去乙酰化酶（hdac）是关键的表观遗传调控因子，在各种癌症中异常表达，使其成为抗癌药物开发的重要靶点。在此，我们提出HDACiAP (https://hdac.kangsgo.cn)，一个精心策划的，全面的数据库和分析平台，专门用于HDAC抑制剂。HDACiAP目前包含32,721种化合物和123,154种生物活性记录，整合了多维数据，如物理化学性质、生物活性、毒性预测、化合物选择性注释、计算生成的对接姿势和蛋白质-配体相互作用谱。该平台集成了内置的分子对接和集成的机器学习和基于深度学习的预测模块，使用户能够评估新化合物的效力。此外，HDACiAP还提供直观的可视化界面、多模式搜索功能以及通过RESTful api进行编程访问。总之，通过提供精心策划的生物活性资源以及计算对接注释和预测模型，HDACiAP为探索HDAC抑制剂的化学空间提供了一个实用的平台，支持初步的虚拟筛选，并促进人工智能辅助发现HDAC靶向化合物。

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来源期刊

Journal of Chemical Information and Modeling 化学-化学综合

CiteScore

9.80

自引率

10.70%

发文量

529

审稿时长

1.4 months

期刊介绍： The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.