Free energy calculations in drug discovery

蛋白质-配体结合自由能是发现和优化新药的一个重要特性。在计算机辅助药物设计的背景下，能够在计算机上计算准确、精确和可重复的结合自由能，对于减少开发候选药物所需的试错合成努力的数量至关重要。更强效和选择性的化合物将与期望的目标强结合，并显示弱结合相关蛋白。最近的商业软件产品已经将绑定自由能计算的使用推向了制药行业的主流开发管道。然而，即使预先筛选了合适的靶系统，也只能在大约一半的情况下达到所需的1千卡摩尔-1的预测精度。目前还不可能先验地确定一个特定系统是否达到了足够的精度，并且需要昂贵且耗时的实验验证。在这个项目中，将探讨这些自由能计算在制药工业背景下的准确性。我们将根据已发表的数据开发一个大型测试系统数据集，并确定计算出的自由能对仿真软件、参数、自由能计算方法和系统设置的敏感性。基于这种敏感性分析，项目目标是双重的：1。找出蛋白质-配体结合自由能计算不准确的主要原因。2. 改进自由能计算方法，解决这些敏感性问题。

Protein-ligand binding free energy is a crucial property used in the discovery and optimisation of novel drugs. In the context of computer-aided drug design, being able to calculate accurate, precise and reproducible binding free energies in silico is essential in terms of reducing the amount of trial-and-error synthetic effort required to develop a candidate drug. More potent and selective compounds will bind strongly to the desired target, and show weak binding to related proteins. Recent commercial software products have pushed the use of binding free energy calculations into mainstream development pipelines within the pharmaceutical industry. Even with pre-screening of suitable target systems, however, the required predictive accuracy of 1 kcal mol-1 is only attained in around half of cases. Determining whether sufficient accuracy has been achieved for a particular system is currently not possible a priori and requires expensive and time-consuming experimental validation. In this project the accuracy of these free energy calculations in the context of the pharmaceutical industry will be explored. We will develop a large dataset of test systems based on published data and determine the sensitivity of the calculated free energies to the choice of simulation software, parameters, free energy calculation method and system setup. Based on this sensitivity analysis, the project objectives are twofold: 1. To identify the primary causes of inaccuracy in protein-ligand binding free energy calculations. 2. To improve free energy calculation methodology to address these sensitivities.