Computational alchemy for molecular design and optimization

分子设计和优化的计算炼金术

基本信息

批准号：
10261348
负责人：
David Lowell Mobley
金额：
$ 33.9万
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 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10261348
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

项目摘要

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.

项目摘要/摘要药物发现既耗时又昂贵，现在每种新药都上市平均成本超过10亿美元。这种成本是由毒品发现的困难驱动的，部分是由在发现初始“命中”过程中涉及的反复试验量，该过程调节了生物分子的功能，然后将它们放入具有足够的生物分子靶标和其他理想目标的“铅”中特性。在这里，我们开发并改善了指导这一过程的计算方法，从而允许潜力预期的效果在创建之前对计算进行了测试 - 大大降低了该过程中涉及的反复试验量并指导分子设计过程。在这里，我们以小组的先前工作为基础，以及基于炼金术自由能计算的领域分子模拟 - 最有希望的指导药物发现的计算技术。然而，此类技术仅在有限的案件中效果很好，需要大量的专业知识才能使员工甚至他们的局限性尚未得到充分理解。在这里，我们专注于扩展可以是用这些技术处理，使计算更加稳健，快速，提高准确性并识别并隔离剩余的维修效果。炼金术自由能计算具有特别的希望，因为它们的准确性和物理实现主义。在这里，我们专注于这些计算的技术和应用，重点是（1）提高效率结合自由能计算的准确性；（2）自由能的自动化和大规模基准测试指导工作以确保鲁棒性和准确性的计算；（3）应用模拟和自由能计算以指导铅发现并优化相扑E-1抑制剂作为潜在的抗癌者毒品。从广义上讲，目标1-2专注于迭代改进和测试计算工具，而AIM 3重点是在与实验合作者的特定应用程序上。这项工作有望具有更广泛的范围，更准确，更快的自由能计算，以便他们可以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产生一个工作流，其中的化学家开发新分子以结合特定目标可能会输入数百个有一天离开工作之前，可以将接下来综合到计算机中的潜在化合物，然后返回工作第二天早晨，根据预测的目标，选择性，选择性，选择这些化合物将自动优先考虑溶解度和其他属性，允许多年的合成和测定。在这里，我们发展，测试和应用技术以帮助使这项工作流通，并以我们以前的广泛成功为基础结合预测的物理建模。这也利用并扩展了我们先前的R01中构建的技术。