Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University , 381 Royal Parade, Parkville, Victoria 3052, Australia.
J Med Chem. 2018 Feb 8;61(3):638-649. doi: 10.1021/acs.jmedchem.7b00681. Epub 2017 Aug 4.
Underpinning all drug discovery projects is the interaction between a drug and its target, usually a protein. Thus, improved methods for predicting the magnitude of protein-ligand interactions have the potential to improve the efficiency of drug development. In this review, we describe the principles of free energy methods used for the calculation of protein-ligand binding free energies, the challenges associated with these methods, and recent advances developed to address these difficulties. We then present case studies from 2005 to 2017, each demonstrating that alchemical free energy methods can assist rational drug design projects. We conclude that alchemical methods are becoming a feasible reality in medicinal chemistry research due to improved computational resources and algorithms and that alchemical free energy predictions methods are close to becoming a mainstream tool for medicinal chemists.
所有药物发现项目的基础都是药物与其靶点（通常是蛋白质）之间的相互作用。因此，改进预测蛋白质-配体相互作用强度的方法有可能提高药物开发的效率。在这篇综述中，我们描述了用于计算蛋白质-配体结合自由能的自由能方法的原理、这些方法所面临的挑战，以及为解决这些困难而开发的最新进展。然后，我们展示了 2005 年至 2017 年的案例研究，每个案例都表明，量子化学自由能方法可以辅助合理的药物设计项目。我们得出结论，由于计算资源和算法的改进，量子化学方法在药物化学研究中正在成为一种可行的现实，并且量子化学自由能预测方法已接近成为药物化学家的主流工具。
