Solvation directed drug design: from molecular physics to lead optimization

项目摘要 该项目旨在开发新的方法和计算工具，以加快基于结构的药物- 发现并应用这些方法来确定新的阿片受体和SARS的候选先导药物 CoV-2主要蛋白水解酶。这将通过提供水化结构的详细分析和 靶向蛋白质结合口袋中的热力学，然后将此信息合并到对接和 基于水的药效团虚拟屏幕。主要目标是开发能够表征和绘制地图的分析工具 药物靶表面上的溶剂化，然后利用这些溶剂化结构和热力学映射来 改进绑定口袋可药性计算方法，对可购买化合物进行虚拟筛选 数据库和Rational Lead修改。一种新的虚拟筛选方法的初步结果 将基于水-蛋白质相互作用的药效团构建与ROCs快速形状和图案相结合 匹配结果表明，该方法比计算对接快3个数量级以上。

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.