Computational alchemy for molecular design and optimization

分子设计和优化的计算炼金术

• 批准号: 9885888
• 负责人: David Lowell Mobley
• 金额: $ 34.35万
• 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 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9885888
• 关键词: Active Sites; Address; Affinity; Antineoplastic Agents; Automation; Back; Benchmarking; Binding; Biological Assay; Breast Cancer Cell; Bypass; Computational Technique; Computers; Computing Methodologies; Consumption; Development; Disease; Employment; Ensure; Equilibrium; Evaluation; Face; Failure; Foundations; Free Energy; Health Benefit; Industrialization; Infrastructure; Lead; Ligand Binding; Ligands; Ligase; Mainstreaming; Malignant Neoplasms; Molecular; Molecular Machines; Motion; Pharmaceutical Preparations; Pharmacologic Substance; Process; Property; Proteins; Public Health; Publications; Rewards; Rotation; Sampling; Series; Solid; Solubility; System; Techniques; Technology; Testing; Time; Ubiquitin; Water; Work; anticancer treatment; base; blind; common treatment; computerized tools; cost; design; drug discovery; flexibility; improved; inhibitor/antagonist; innovation; lead optimization; molecular dynamics; mutant; novel anticancer drug; novel therapeutics; overexpression; physical model; predictive test; prospective; repaired; screening; simulation; small molecule; success; tool; tumor

PROJECT SUMMARY/ABSTRACT Pharmaceutical drug discovery is time-consuming and expensive, with each new drug brought to market now costing well over $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. Here, we develop and improve computational methods to guide this process, allowing the potential efficacy of prospective leads to be tested computationally prior to their creation — dramatically reducing the amount of trial and error involved in the process and guiding the molecular design process. Here, we build on previous work in the group and the field on alchemical free energy calculations based on molecular simulations — the most promising present computational technique for guiding drug discovery. However, such techniques work well only for a limited subset of cases, require considerable expertise to employ, and even their limitations are not yet well understood. Here, we focus on expanding the range of systems which can be treated with these techniques, making the calculations more robust and rapid, improving accuracy, and identifying and isolating remaining deficiencies for repair. Alchemical free energy calculations hold particular promise both because of their accuracy and physical real- ism. Here, we focus on technology and applications of these calculations, focusing on (1) improved efficiency and accuracy of binding free energy calculations; (2) automation and large-scale benchmarking of free energy calculations to guide work to ensure robustness and accuracy; and (3) applications to utilizing simulations and free energy calculations to guide lead discovery and optimization of SUMO E-1 inhibitors as potential anti-cancer drugs. Broadly, Aims 1-2 focus on iteratively improving and testing computational tools, whereas Aim 3 focuses on a specific application with experimental collaborators. This work promises more accurate and more rapid free energy calculations, with broader scope so that they can reliably be applied to molecular design problems in drug discovery and elsewhere. Our long-term work aims to produce a workflow where a chemist developing new molecules to bind a particular target could input hundreds of potential compounds to synthesize next into a computer before leaving work one day, and return to work the following morning to find these compounds automatically prioritized based on predicted target affinity, selectivity, solubility and other properties, allowing years worth of synthesis and assays to be bypassed. Here, we develop, test and apply technologies to help make this workflow possible, building on our extensive previous success in physical modeling for binding prediction. This also leverages and extends technologies built in our prior R01.

项目总结/摘要 制药药物的发现是耗时和昂贵的，现在每一种新药都被推向市场 平均花费超过10亿美元。这一成本是由药物发现的困难造成的，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， 然后将其提炼成对生物分子靶点有足够亲和力的“引线”， 特性.在这里，我们开发和改进计算方法来指导这一过程， 在创建潜在销售线索之前，通过计算测试潜在销售线索的有效性-极大地减少 大量的试验和错误涉及的过程和指导分子设计过程。 在这里，我们以小组和炼金术自由能计算领域之前的工作为基础，基于 分子模拟-目前最有前途的指导药物发现的计算技术。然而，在这方面， 这种技术仅对有限的情况子集有效，需要相当多的专业知识来使用，甚至 它们的局限性尚未得到很好的理解。在这里，我们专注于扩大系统的范围， 处理这些技术，使计算更加强大和快速，提高准确性，并确定 并隔离剩余的缺陷进行修复。 炼金术的自由能计算特别有希望，因为它们的准确性和物理上的真实的- 主义。在这里，我们专注于这些计算的技术和应用，重点是（1）提高效率 自由能计算的自动化和大规模基准测试 计算，以指导工作，以确保鲁棒性和准确性;（3）应用程序，利用模拟和 自由能计算指导SUMO E-1抑制剂作为潜在抗癌药物的先导发现和优化 毒品概括地说，目标1-2侧重于迭代地改进和测试计算工具，而目标3侧重于 在一个特定的应用程序与实验合作者。 这项工作承诺更准确和更快速的自由能计算，更广泛的范围，使他们能够 可靠地应用于药物发现和其他地方的分子设计问题。我们的长期工作目标是 产生一个工作流程，一个化学家开发新的分子来结合一个特定的目标， 在一天下班前，将可能合成的化合物输入计算机，然后返回工作岗位， 第二天早上，根据预测的目标亲和力，选择性， 溶解度和其他性质，从而可以绕过多年的合成和分析。在这里，我们发展， 测试和应用技术，以帮助使这项工作尽快成为可能，建立在我们以前的广泛成功的基础上， 结合预测的物理建模。这也利用和扩展了我们以前的R 01中内置的技术。