A Web-Based Automatic Virtual Screening System

基于网络的自动虚拟筛选系统

• 批准号: 10612058
• 负责人: John J. Irwin
• 金额: $ 33.92万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10612058
• 关键词: 2019-nCoV; Address; Alloys; Animals; Benchmarking; Biological; Biological Assay; Biology; Chemicals; Chemistry; Clinical Trials; Communities; Databases; Development; Docking; Drug Targeting; Excipients; Food; Food Additives; Goals; Hand; Informatics; Knee; Laboratories; Letters; Libraries; Lysine; Mechanics; Methods; Online Systems; Peptide Hydrolases; Pharmaceutical Preparations; Pharmacology; Quantum Mechanics; Reaction; Reagent; Research; Research Personnel; Science; Statistical Mechanics; Structure; System; TMPRSS2 gene; Techniques; Testing; Time; Trust; Vendor; Work; analog; drug discovery; informatics tool; inhibitor; interest; physical property; programs; side effect; small molecule; tool; virtual; virtual library; virtual screening

PROJECT SUMMARY / ABSTRACT A long-term goal is to bring small molecules to biologists and chemical biologists, developing easy-to-use tools and libraries that rapidly identify reagents. A second goal uses these libraries and tools to predict biological activity for key compound classes, advancing the science and demonstrating proof-of-concept. The tools introduced by this research program have become central to virtual screening. The ZINC database is the most widely used compound library in the field, while our DUD and DUD-E benchmarks are ubiquitous in virtual screening. Recently, our development of ultra-large libraries has been embraced by the field. The Similarity Ensemble Approach (SEA) brings chemoinformatic target prediction to a large community, and we have used it to predict drug off-targets, their side effects, and the activities of supposedly inert molecules. Here we extend both projects, further developing community libraries and tools in aim 1, applying these to the prediction of biological activities in aim 2. The specific aims are: Aim 1. New tools to bring chemistry to biology. An exciting result of the last period was the introduction of ultra-large libraries. While an accessible library of >20 billion molecules has expanded our horizons, the two component reactions from which they derive are inevitably limiting. We will A. develop a “chemistry commons” of more elaborate virtual molecules available from academic labs, testing them in aim 2, B. expand the chemistry available for covalent docking to develop new community-accessible libraries of selective electrophiles for covalent inhibitor discovery, C. We will optimize the widely-used DUDE benchmarks, introducing new subsets to address the biases that they certainly still retain. D. We will integrate into ZINC methods that enable similarity searches for analogs in sublinear time. Aim 2. Libraries of high value compounds, and their activities. We will A. test the utility of more elaborate virtual libraries from aim 1 where they are experimentally tested, B. test the new covalent electrophilic libraries in docking campaigns against SARS-2 relevant proteases 3CLPro and TMPRSS2. C. expand our interest in target discovery by chemoinformatics, focusing on compounds that are widely used in biology because they are inactive: drug excipients and Generally Regarded As Safe food additives. D. ask whether GRAS molecules have on-target pharmacology, as we found with drug excipients, testing our predictions experimentally. Whereas these goals are ambitious, extensive preliminary results support their feasibility.

项目摘要/摘要 一个长期的目标是将小分子带给生物学家和化学生物学家，开发出易于使用的 快速识别试剂的工具和库。第二个目标是使用这些库和工具来预测 关键化合物类的生物活性，促进科学和示范概念验证。 这项研究计划引入的工具已经成为虚拟筛查的核心。锌数据库 是该领域中使用最广泛的复合库，而我们的DUD和DUD-E基准测试在 虚拟放映。近年来，我国超大型图书馆的发展受到了业界的普遍欢迎。这个 相似集成方法(SEA)将化学信息目标预测带到一个大的社区，我们 用它来预测药物的脱靶作用，它们的副作用，以及所谓的惰性分子的活动。 在这里，我们扩展了这两个项目，进一步开发了Aim 1中的社区库和工具，并将它们应用于 目标2中的生物活动预测。具体目标是： 目标1.将化学带入生物学的新工具。上一阶段的一个令人兴奋的结果是引入了 超大型图书馆。虽然一个可访问的200亿分子图书馆扩大了我们的视野，但这两个 它们衍生出的成分反应不可避免地是有限的。我们将建立一个“化学公地”。 从学术实验室获得的更复杂的虚拟分子，在Aim 2，B中进行测试。 可用于共价对接的化学物质，以开发新的社区可访问的选择性图书馆 用于发现共价抑制剂的亲电材料，C.我们将优化广泛使用的DUD基准， 引入新的子集来解决它们肯定仍然保留的偏见。D.我们将整合到锌中 允许在次线性时间内进行相似性搜索的方法。 目标2.高价值化合物文库及其活性。我们将测试更详细的 来自Aim 1的虚拟文库，在那里进行实验测试，B.测试新的共价亲电文库 在与SARS-2相关的蛋白酶3CLPro和TMPRSS2的对接活动中。C.扩大我们对……的兴趣 通过化学信息学发现目标，重点关注在生物学中广泛使用的化合物，因为它们 无活性：药物辅料，一般被认为是安全的食品添加剂。D.询问狂欢节是否 就像我们在药物辅料中发现的那样，分子具有靶向的药理作用，这验证了我们的预测 试验性的。 尽管这些目标雄心勃勃，但广泛的初步结果支持它们的可行性。

项目成果

Docking and chemoinformatic screens for new ligands and targets.

新的配体和靶标的对接和化学信息筛选。

• DOI: 10.1016/j.copbio.2009.08.003
• 发表时间: 2009-08
• 期刊: CURRENT OPINION IN BIOTECHNOLOGY
• 影响因子: 7.7
• 作者: Kolb, Peter;Ferreira, Rafaela S.;Irwin, John J.;Shoichet, Brian K.
• 通讯作者: Shoichet, Brian K.

Prediction and evaluation of protein farnesyltransferase inhibition by commercial drugs.

• DOI: 10.1021/jm901613f
• 发表时间: 2010-03-25
• 期刊: Journal of medicinal chemistry
• 影响因子: 7.3
• 作者: DeGraw AJ;Keiser MJ;Ochocki JD;Shoichet BK;Distefano MD
• 通讯作者: Distefano MD

ZINC 15--Ligand Discovery for Everyone.

• DOI: 10.1021/acs.jcim.5b00559
• 发表时间: 2015-11-23
• 期刊: Journal of chemical information and modeling
• 影响因子: 5.6
• 作者: Sterling T;Irwin JJ
• 通讯作者: Irwin JJ

Docking Screens for Novel Ligands Conferring New Biology.

• DOI: 10.1021/acs.jmedchem.5b02008
• 发表时间: 2016-05-12
• 期刊: Journal of medicinal chemistry
• 影响因子: 7.3
• 作者: Irwin JJ;Shoichet BK
• 通讯作者: Shoichet BK

Virtual ligand screening against comparative protein structure models.

针对比较蛋白质结构模型的虚拟配体筛选。

• DOI: 10.1007/978-1-61779-465-0_8
• 发表时间: 2012
• 期刊: Methods in molecular biology (Clifton, N.J.)
• 作者: Fan,Hao;Irwin,JohnJ;Sali,Andrej
• 通讯作者: Sali,Andrej