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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

基本信息

批准号：
8918691
负责人：
David Lowell Mobley
金额：
$ 27.95万
依托单位：
UNIVERSITY OF CALIFORNIA-IRVINE
依托单位国家：
美国
项目类别：
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/8918691
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 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 research study screening simulation small molecule tool

项目摘要

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.

药物研发是一项耗时且昂贵的工作，目前每种新药上市的平均成本约为10亿美元。这种成本是由药物发现的困难所驱动的，并且部分地由发现调节生物分子功能的初始“命中”的过程中所涉及的tria和错误的量所驱动，然后将这些改进为对生物分子靶标具有足够亲和力和其他期望性质的“先导物”。理想情况下，计算方法可以指导这一过程，通过在实验之前提出命中建议并预测将其改进为先导化合物的化学修饰，减少所涉及的试错量，从而增强亲和力，同时保持药物样性质。但是目前的计算方法不足以以这种方式改变发现过程。最近基于分子模拟的炼金术自由能计算的创新在达到帮助药物发现所需的准确性水平方面显示出相当大的希望，但这些模拟需要相当多的专业知识来设置和进行，以及大量的计算机能力。该提案的重点是降低这些障碍，提供一种新的方法来自动规划和设置这些计算，并提高计算效率。炼金术自由能计算是物理上最现实的计算方法之一，也是最有希望的准确性之一。该项目的目标是：（1）开发一种新的工具，用于自动设置相对结合自由能的计算，以优化药物先导化合物;（2）有效地计算配体结合模式的占用率，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组蛋白甲基转移酶抑制剂的实验药物发现，其作为潜在的抗癌药物显示出相当大的前景。虽然相当大的努力已经进入炼金术的自由能计算，这项工作的一个创新方面是集中在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，然后包括所有这些在结合自由能计算，使个别抑制剂的结合结构不需要事先知道。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中的问题。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，而这项工作为该方向迈出了重要一步。