A hybrid molecular simulation/machine-learning framework for rapid and accurate computation of absolute binding free energies of lead-like molecules

自由能微扰（FEP）方法越来越多地用于指导临床前候选化合物的硅效价优化。FEP最常用于相对结合自由能（RBFE）计算，非常适合药物发现活动的hit-to-lead或先导优化阶段。然而，RBFE方法仅限于计算结构相关分子之间结合亲和力的差异。非常希望开发出与RBFE相当的准确性，但适用于更广泛类别的药物设计问题的方法。超出RBFE范围的高价值问题的例子包括：结合模式的预测；不同化学型的排序；结合选择性谱的预测。这些问题原则上可以用绝对束缚自由能（ABFE）计算方法来解决。然而，ABFE目前被认为计算量太大，不可靠，无法广泛应用。该项目将利用米歇尔实验室的初步结果，大幅提高ABFE计算的效率。与Cole实验室合作，将设计新的模拟协议，将gpu加速分子动力学模拟与力场和采样算法的机器学习相结合。这些方案将以阿斯利康感兴趣的各种蛋白质配体数据集为基准。总体目标是使ABFE计算足够快速和准确，以便在工业研发中常规使用。这是开发下一代计算机辅助药物设计软件和方法的一个令人兴奋的机会。

Free Energy Perturbation (FEP) methods are increasingly used to guide in silico potency optimisation of preclinical candidate compounds. FEP is most commonly used in the context of Relative Binding Free Energy (RBFE) calculations that are well suited to hit-to-lead or lead optimisation stages of a drug discovery campaign. However, RBFE methods are limited to the calculation of differences in binding affinity between structurally related molecules. It is highly desirable to develop methodologies that achieve accuracy comparable to RBFE but are applicable to a broader class of drug design problems. Examples of high value problems outside the scope of RBFE include: prediction of binding modes; ranking of diverse chemotypes; prediction of binding selectivity profiles. Such problems can in principle be tackled using Absolute Binding Free Energy (ABFE) calculation methods. However ABFE are currently considered too computationally intensive and unreliable to be widely used.This project will leverage preliminary results from the Michel lab to substantially increase the efficiency of ABFE calculations. In collaboration with the Cole lab, new simulation protocols that combine GPU-accelerated molecular dynamics simulations with machine learning of forcefields and sampling algorithms will be devised. The protocols will be benchmarked on diverse protein-ligand datasets of interest to AstraZeneca. The overall aim is to make ABFE calculations sufficiently rapid and accurate to enable routine use in industrial R&D. This is an exciting opportunity to develop next-generation computer-aided drug design software and methodologies.