Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China.
Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 LingLing Road, Shanghai 200032, P. R. China.
J Chem Inf Model. 2024 Apr 8;64(7):2817-2828. doi: 10.1021/acs.jcim.3c00395. Epub 2023 May 11.
Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease with a broad spectrum of histologic manifestations. The rapidly growing prevalence and the complex pathologic mechanisms of NAFLD pose great challenges for treatment development. Despite tremendous efforts devoted to drug development, there are no FDA-approved medicines yet. Here, we present NAFLDkb, a specialized knowledge base and platform for computer-aided drug design against NAFLD. With multiperspective information curated from diverse source materials and public databases, NAFLDkb presents the associations of drug-related entities as individual knowledge graphs. Practical drug discovery tools that facilitate the utilization and expansion of NAFLDkb have also been implemented in the web interface, including chemical structure search, drug-likeness screening, knowledge-based repositioning, and research article annotation. Moreover, case studies of a knowledge graph repositioning model and a generative neural network model are presented herein, where three repositioning drug candidates and 137 novel lead-like compounds were newly established as NAFLD pharmacotherapy options reusing data records and machine learning tools in NAFLDkb, suggesting its clinical reliability and great potential in identifying novel drug-disease associations of NAFLD and generating new insights to accelerate NAFLD drug development. NAFLDkb is freely accessible at https://www.biosino.org/nafldkb and will be updated periodically with the latest findings.
非酒精性脂肪性肝病 (NAFLD) 是最常见的慢性肝脏疾病,具有广泛的组织学表现。NAFLD 患病率的迅速增长和复杂的病理机制给治疗开发带来了巨大的挑战。尽管在药物开发方面做出了巨大努力,但目前还没有获得 FDA 批准的药物。在这里,我们介绍了 NAFLDkb,这是一个针对 NAFLD 的计算机辅助药物设计的专业知识库和平台。NAFLDkb 从各种来源材料和公共数据库中整理了多方面的信息,将与药物相关的实体呈现为单独的知识图谱。该网络界面还实现了实用的药物发现工具,以促进 NAFLDkb 的利用和扩展,包括化学结构搜索、类药性筛选、基于知识的重新定位和研究文章注释。此外,本文还介绍了知识图谱重新定位模型和生成式神经网络模型的案例研究,其中使用了 NAFLDkb 中的数据记录和机器学习工具,重新确定了三个重新定位的候选药物和 137 种新型类药性化合物,作为 NAFLD 治疗的选择,这表明其在识别新的药物-疾病关联和加速 NAFLD 药物开发方面具有临床可靠性和巨大潜力。NAFLDkb 可在 https://www.biosino.org/nafldkb 上免费获取,并将定期更新最新发现。