Alchemical free energy methods for efficient drug lead optimization

描述（由申请人提供）：药物发现是耗时且昂贵的，现在每一种新药推向市场平均花费约10亿美元。这种成本是由药物发现的困难所驱动的，部分原因是在寻找调节生物分子功能的初始“命中”，然后将其提炼成对生物分子目标具有足够亲和力和其他理想特性的“先导”过程中所涉及的反复试验和错误。理想情况下，计算方法可以指导这一过程，通过在实验前提出命中点和预测化学修饰来减少试验和错误的数量，这些化学修饰将把这些改进为先导，在保持药物性质的同时增强亲和力。但目前的计算方法还不足以改变这种发现过程。最近在基于分子模拟的炼金术自由能计算方面的创新显示出相当大的希望，可以达到帮助药物发现所需的精确水平，但这些模拟需要相当多的专业知识来建立和实施，以及大量的计算机能力。该方案旨在降低这些障碍，提供一种自动规划和设置这些计算的新方法，并提高计算效率。炼金术自由能计算是一种物理上最现实的计算方法，在准确性方面也是最有前途的方法之一。该项目的目标是：(1)开发一种新工具，用于自动设置药物先导物优化的相对结合自由能计算；(2)有效计算配体结合模式占有率，显著降低结合自由能预测的计算费用；(3)利用这些技术指导组蛋白甲基转移酶抑制剂的实验药物发现，这些抑制剂作为潜在的抗癌药物具有相当大的前景。虽然在炼金术自由能计算方面已经付出了相当大的努力，但这项工作的一个创新方面是关注于预测结合模式和结合亲和力。这是通过使用快速对接方法，结合探索性模拟来处理的，以确定各种稳定的配体结合模式，然后在结合自由能计算中包括所有这些，因此不需要事先知道单个抑制剂的结合结构。这项工作将加速有前途的亲和力计算工具，并提高自动化程度，使它们更容易应用于药物发现问题。这些技术的长期目标是通过提供强大的计算亲和力预测来改变早期药物发现过程，而这项工作在这个方向上迈出了重要的一步。

DESCRIPTION (provided by applicant): Pharmaceutical drug discovery is time-consuming and expensive, with each new drug brought to market now costing roughly $1 billion on average. This cost is driven by the difficulty of drug discovery, and in part by the amount of tria and error involved in the process of finding initial "hits" which modulate the function of a biomolecule, and then refining these into "leads" which have adequate affinity for the biomolecular target and other desirable properties. Computational methods ideally could guide this process, reducing the amount of trial and error involved by suggesting hits in advance of experiment and predicting chemical modifications which will improve these into leads, enhancing affinity while maintaining drug-like properties. But current computational methods are not adequate to change the discovery process in this way. Recent innovations in alchemical free energy calculations based on molecular simulations show considerable promise at reaching the level of accuracy needed to help drug discovery, but these simulations require considerable expertise to set up and conduct, and a great deal of computer power. This proposal focuses on lowering these barriers, providing a new approach to automatically plan and set up these calculations, and improved computational efficiency. Alchemical free energy calculations are one of the most physically realistic computational approaches available, and one of the most promising in terms of accuracy. This project's aims are to (1) develop a new tool to automate setup of relative binding free energy calculations for drug lead optimization; (2) efficiently calculate ligand binding mode occupancies, dramatically reducing the computational expense of binding free energy predictions; and (3) use these techniques to guide experimental drug discovery of histone methyltransferase inhibitors, which show considerable promise as potential anti-cancer drugs. While considerable effort has gone into alchemical free energy calculations, one innovative aspect of this work is the focus on predicting binding mode as well as binding affinity. This is handled by using fast docking methods, in combination with exploratory simulations, to identify a variety of stable ligand binding modes, then including all of these in binding free energy calculations, so that bound structures of individual inhibitors need not be known in advance. This work will speed up promising tools for affinity calculation, and improve automation so that they can more easily be applied to problems in drug discovery. The long-term goal of these techniques is to change the early stage drug discovery process by providing robust computational affinity predictions, and this work provides an important step in that direction.