Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations
Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations
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DOI：
10.1021/acs.jcim.7b00564
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发表时间：
2017-12-01
影响因子：
5.6
通讯作者：
Sherman, Woody
中科院分区：
文献类型：
--
作者：
Cournia, Zoe;Allen, Bryce;Sherman, Woody
几十年来，准确的蛋白质-配体结合亲和力的计算机预测一直是基于结构的药物设计的主要目标，因为它将为药物发现过程带来假定的价值。然而，由于各种科学、技术和实践挑战，计算方法在现实世界的药物发现应用中一直未能提供价值。最近，一系列通常被称为相对结合自由能（RBFE）计算的方法，依赖于基于物理的分子模拟和统计力学，在药物发现项目的背景下可靠地产生准确的预测。这种进步源于基础科学方法（对力场和采样算法的数十年研究）的积累发展，以及计算资源（图形处理单元和云基础设施）的巨大增长。来自回顾性验证研究、盲挑战预测和前瞻性应用的越来越多的证据表明，RBFE模拟现在可以以足够的准确性和吞吐量预测同源配体的亲和差异，从而在hit-to-lead和lead优化工作中提供可观的价值。在这里，我们概述了当前RBFE的实现，强调了最近的进展和仍然存在的挑战，以及强调获得可靠的RBFE结果的实际考虑因素的示例。我们特别关注相对结合自由能，因为计算比绝对结合自由能（ABFE）计算更少的计算强度，并直接映射到hit-to-lead和lead优化过程，其中参考分子和新想法（虚拟分子）之间的相对结合能预测可用于优先合成分子。我们从理论和应用的角度描述了运行RBFE计算的关键方面，结合了回顾性文献示例和药物发现项目的前瞻性研究。这项工作旨在提供与运行相对结合自由能模拟相关的科学，技术和实际问题的当代概述，重点是现实世界的药物发现应用。我们为提高RBFE模拟的准确性提供了指导方针，特别是针对具有挑战性的案例，并强调了可以通过进一步研究来改进的未解决问题。
Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directiy onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives, using a combination of retrospective literature examples and prospective studies from drug discovery projects. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative binding free energy simulations, with a focus on real-world drug discovery applications. We offer guidelines for improving the accuracy of RBFE simulations, especially for challenging cases, and emphasize unresolved issues that could be improved by further research in the field.