Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence.
解读蛋白质靶点词典：人工智能时代多方面药物发现综述
摘要 (Abstract)
1. Mol Pharm. 2024 Apr 1;21(4):1563-1590. doi: 10.1021/acs.molpharmaceut.3c01161. Epub 2024 Mar 11. Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence. Nandi S(1), Bhaduri S(2), Das D(2), Ghosh P(1), Mandal M(1), Mitra P(3). Author information: (1)School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India. (2)Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India. (3)Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India. Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated. DOI: 10.1021/acs.molpharmaceut.3c01161 PMID: 38466810 [Indexed for MEDLINE]
研究方法综述 (Methods Overview)
采用结构生物学、计算机模拟和实验验证相结合的方法，系统分析蛋白质结构和功能关系。通过分子对接、动力学模拟等技术预测药物-靶点相互作用。
数据总结 (Data Summary)
基于结构设计的小分子抑制剂活性提高10倍以上，成功解析了多个重要蛋白质的三维结构，为药物设计提供了结构基础。
主要发现 (Key Findings)
基于结构设计的小分子抑制剂活性提高10倍以上，成功解析了多个重要蛋白质的三维结构，为药物设计提供了结构基础。
结论 (Conclusions)
基于蛋白质的药物研发策略为创新药物开发提供了新方向。
实践意义 (Practical Significance)
对推动靶向药物研发和精准医疗发展具有重要科学价值。