Solvation directed drug design: from molecular physics to lead optimization

溶剂化导向药物设计：从分子物理学到先导化合物优化

• 批准号: 10330792
• 负责人: Thomas Philip Kurtzman
• 金额: $ 37.96万
• 依托单位: HERBERT H. LEHMAN COLLEGE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10330792
• 关键词: 2019-nCoV; Binding; Binding Proteins; Computer software; Computing Methodologies; Databases; Disease; Docking; Drug Design; Drug Targeting; Hydration status; Lead; Maps; Membrane Proteins; Methods; Modification; Molecular; Pattern; Peptide Hydrolases; Pharmaceutical Preparations; Physics; Process; Proteins; Scientist; Shapes; Speed; Structure; Surface; Thermodynamics; Water; Work; base; computerized tools; design; drug candidate; drug discovery; improved; lead optimization; mu opioid receptors; pharmacophore; tool; virtual; virtual screening

Project Summary This project aims to develop new methods and computational tools that will speed structure-based drug- discovery and apply these methods to identify new lead drug candidates for the mu-opioid receptor and SARS- CoV-2 main proteases. This will be accomplished by providing a detailed analysis of hydration structure and thermodynamics in targeted protein binding pockets then incorporating this information into docking and water-based pharmacophore virtual screens. Key aims are to develop analysis tools that characterize and map out solvation on the surfaces of drug target then utilize these solvation structural and thermodynamic maps to improve computational methods of binding pocket druggability, virtual screening of purchasable compound databases, and rational lead modification. Preliminary results for a new method of virtual screening that combines constructing pharmacophores based on water-protein interactions with ROCS fast shape and pattern matching show the method is greater than 3 orders of magnitude faster than computational docking.

项目概要 该项目旨在开发新的方法和计算工具，以加速基于结构的药物开发 发现并应用这些方法来识别 mu-阿片受体和 SARS-的新的主要候选药物 CoV-2 主要蛋白酶。这将通过提供水合结构的详细分析和 目标蛋白质结合袋中的热力学，然后将这些信息纳入对接和 水基药效团虚拟屏幕。主要目标是开发能够表征和映射的分析工具 药物靶标表面的溶剂化，然后利用这些溶剂化结构和热力学图来 改进结合袋成药性的计算方法，可购买化合物的虚拟筛选 数据库和合理的先导修饰。虚拟筛选新方法的初步结果 将基于水-蛋白质相互作用构建药效团与 ROCS 快速形状和模式相结合 匹配表明该方法比计算对接快3个数量级以上。