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.

制药药物的发现是耗时和昂贵的，每一个新药 现在投入市场的平均成本约为10亿美元。这一成本是由困难驱动的 药物发现的效率，部分取决于发现过程中所涉及的试验和错误的数量 最初的“命中”，调节生物分子的功能，然后将其提炼成“线索” 其对生物分子靶具有足够的亲和力和其它所需的性质。 理想情况下，计算方法可以指导这一过程，减少试验和错误的数量 在实验前提示命中并预测化学修饰 这将使这些物质转化为先导物，在保持药物样性质的同时增强亲和力。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 在达到帮助药物发现所需的准确性水平方面表现出相当大的希望， 这些模拟需要相当多的专业知识来建立和进行， 计算机的力量该提案的重点是降低这些障碍，提供一种新的方法， 自动计划和设置这些计算，并提高了计算效率。 炼金术自由能的计算是物理上最现实的计算之一 方法可用，并且在准确性方面是最有前途的方法之一。该项目的目标 是（1）开发一种新的工具，以自动设置相对结合自由能计算， 药物先导优化;（2）高效计算配体结合模式， 减少结合自由能预测的计算费用;和（3）使用这些 技术来指导组蛋白甲基转移酶抑制剂的实验药物发现， 显示出作为潜在抗癌药物的相当大的前景。 虽然相当大的努力已经进入炼金术的自由能计算，一个创新的 这项工作的重点是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，以确定 各种稳定的配体结合模式，然后包括所有这些结合自由能 计算，因此不需要预先知道单个抑制剂的结合结构。 这项工作将加快有前途的亲和力计算工具，并提高自动化， 它们可以更容易地应用于药物发现的问题。这些长期目标 技术是改变早期阶段的药物发现过程， 计算亲和预测，这项工作提供了一个重要的一步，在这个方向上。