Alchemical free energy methods for efficient drug lead optimization

描述(由申请人提供)：药物发现既耗时又昂贵，目前每种新药推向市场的平均成本约为10亿美元。这一成本是由药物发现的难度驱动的，部分原因是在寻找调节生物分子功能的初始“命中”的过程中涉及的Tria和错误的数量，然后将其提炼成与生物分子靶标和其他所需特性具有足够亲和力的“线索”。理想情况下，计算方法可以指导这一过程，通过在实验前建议命中和预测化学修饰来减少所涉及的试错量，从而将这些改进为先导，在保持药物性质的同时增强亲和力。但目前的计算方法不足以以这种方式改变发现过程。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，在达到帮助药物发现所需的精确度方面有相当大的希望，但这些模拟需要相当多的专业知识来建立和执行，以及大量的计算机能力。这项提议的重点是降低这些障碍，提供一种自动计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现有的物理上最真实的计算方法之一，也是精度方面最有希望的方法之一。该项目的目标是(1)开发一种新的工具来自动设置相对结合自由能计算以用于药物前导优化；(2)有效地计算配体结合模式占有率，大大减少结合自由能预测的计算费用；以及(3)使用这些技术来指导实验药物发现组蛋白甲基转移酶抑制剂，这些药物显示出相当大的潜在抗癌药物的前景。虽然在炼金术自由能计算方面已经做出了相当大的努力，但这项工作的一个创新方面是专注于预测结合模式以及结合亲和力。这是通过使用快速对接方法，结合探索性模拟来识别各种稳定的配体结合模式，然后将所有这些模式都包括在结合自由能计算中来处理的，因此不需要事先知道单个抑制剂的结合结构。这项工作将加快有前景的亲和力计算工具的速度，并提高自动化程度，以便它们可以更容易地应用于药物发现中的问题。这些技术的长期目标是通过提供稳健的计算亲和力预测来改变早期药物发现过程，这项工作提供了朝着这个方向迈出的重要一步。

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.