Alchemical free energy methods for efficient drug lead optimization

用于高效先导药物优化的炼金自由能方法

• 批准号: 8613366
• 负责人: David Lowell Mobley
• 金额: $ 28.03万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8613366
• 关键词: Affect; Affinity; Algorithms; Antineoplastic Agents; Automation; Binding; Binding Sites; Charge; Chemicals; Computers; Computing Methodologies; Development; Disease; Docking; Drug Industry; Ethanol; Face; Failure; Family suidae; Free Energy; Goals; Head; Health Benefit; Hydration status; Individual; Lead; Letters; Libraries; Ligand Binding; Liver; Marketing; Methods; Methyltransferase; Modeling; Modification; Molecular; Molecular Machines; Pharmaceutical Preparations; Pharmacologic Substance; Process; Property; Proteins; Public Health; Relative (related person); Rewards; Sampling; Speed; Staging; Structure; Techniques; Testing; Time; Work; base; blind; common treatment; cost; drug discovery; esterase; falls; histone methyltransferase; improved; in vivo; inhibitor/antagonist; innovation; lead series; molecular dynamics; novel strategies; physical property; public health relevance; research study; screening; simulation; small molecule; tool

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 trial 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.

药物发现既费时又昂贵，每种新药 现在，市场平均耗资约10亿美元。这一成本是由困难驱动的 毒品发现，部分是由于发现过程中涉及的反复试验量 初始“命中”调节生物分子的功能，然后将其精炼成“铅” 对生物分子靶标和其他理想特性具有足够的亲和力。 理想情况下，计算方法可以指导这一过程，减少反复试验量 通过在实验之前提出命中并预测化学修饰，参与 这将改善这些铅，增强亲和力，同时维持类似药物的特性。但 当前的计算方法不足以以这种方式改变发现过程。 基于分子模拟的炼金术自由能计算的最新创新 在达到帮助发现药物所需的准确性水平方面表现出巨大的希望，但是 这些模拟需要大量的专业知识才能建立和进行，并且很多 计算机电源。该提议着重于降低这些障碍，为 自动计划并设置这些计算，并提高计算效率。 炼金术自由能计算是最逼真的计算之一 可用的方法，也是准确性最有前途的方法之一。这个项目的目的是 是（1）开发一种新工具，以自动化相对结合的自由能计算的设置 药物铅优化； （2）有效计算配体结合模式占用率，急剧 减少结合自由能预测的计算费用； （3）使用这些 指导组织甲基转移酶抑制剂的实验药物发现的技术， 作为潜在的抗癌药物表现出巨大的希望。 虽然大量努力进行了炼金术自由能计算，但一种创新的 这项工作的方面是预测结合模式和结合亲和力的重点。这是 通过使用快速对接方法与探索性模拟结合使用来确定 各种稳定的配体结合模式，然后将所有这些都包括在结合自由能中 计算，因此不必事先知道单个抑制剂的结构。 这项工作将加快有希望的工具来进行亲和力计算，并改善自动化，因此 它们可以更容易地应用于药物发现中的问题。这些的长期目标 技术是通过提供鲁棒来改变早期药物发现过程 计算亲和力预测，这项工作为该方向提供了重要的一步。