AN OPEN RESOURCE TO ADVANCE COMPUTER-AIDED DRUG DESIGN

推进计算机辅助药物设计的开放资源

• 批准号: 8756082
• 负责人: Rommie E Amaro
• 金额: $ 72.84万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8756082
• 关键词: Academia; Address; Affinity; Archives; Back; Benchmarking; Binding; Binding Proteins; Biochemical; Biological Assay; Blinded; Calorimetry; Collaborations; Collection; Communities; Computational algorithm; Computer Assisted; Computers; Computing Methodologies; Coupled; Data; Data Collection; Data Quality; Data Set; Databases; Development; Disease; Docking; Drug Design; Drug Industry; Education and Outreach; Educational workshop; Exercise; Genbank; Goals; In Vitro; Industry; Intellectual Property; Internet; Laboratories; Laboratory Research; Ligands; Measurement; Mediation; Methodology; Methods; Modeling; Outcome; Pharmaceutical Preparations; Pharmacologic Substance; Physics; Problem Solving; Process; Property; Proteins; PubChem; Quality of life; Research; Research Contracts; Research Personnel; Resources; Safety; Scientist; Site-Directed Mutagenesis; Source; Speed; Sum; System; Technology; Testing; Thermodynamics; TimeLine; Training Activity; Trust; United States National Institutes of Health; Universities; Validation; Vision; Work; base; blind; cost; cost effective; data management; design; drug discovery; experience; federated computing; flexibility; improved; innovation; insight; interest; meetings; method development; molecular recognition; novel; outreach; protein folding; public health relevance; repository; research study; screening; small molecule; tool; user-friendly; web site

DESCRIPTION (provided by applicant): The application of computer-aided drug design (CADD) to drug discovery has yet to reach its full potential despite the broad use of these methods across academia and the pharmaceutical industry and tremendous progress in CPU speeds over the last 35 years. Although the existing computational methods are useful, there are serious limitations in the ability to predict small molecule ligand-protein target interactions It is recognized that if these obstacles can be overcome, the ability to predict these interactions accurately would have a dramatic and positive outcome through a reduction in small molecule drug discovery timelines and potentially toxic off-target effects, thereby reducing overall development costs and increasing safety of new medications. The computer-aided drug design community is working to develop improved methods and generally agrees that further progress requires greater public availability of high quality, compelling and "problem" specific protein-ligand datasets for challenging, improving and validating computational algorithms. The NIH seeks to solve this problem through the issuance of RFA GM-08-008, "Drug Docking and Screening Data Resource". We submit a proposal to establish a publicly available Drug Design Data Resource (D3R) to meet the goals of this RFA. We propose three innovative CADD community oriented Aims. First we will engage our pharmaceutical partners to identify, curate and enhance 6-10 protein- ligand datasets per year. This work will build on the existing CSAR project both through rapid incorporation of their datasets and perpetuating their academic-industry relationships. An innovative extension beyond CSAR will be longer tenures at pharmaceutical companies to allow further exchange of ideas and testing of data with various workflows, workflows that can be made publicly available. We will engage contract research organizations for compound synthesis and in vitro biochemical assays and our academic partners for novel thermodynamic data. Second, we will use these new datasets as a basis for engaging the CADD community in quarterly blind prediction exercises, focusing on binding mode and affinity predictions for ligand-protein interactions. This blind challenge approach has proven highly fruitful in other fields, e.g. protein folding. Workshops will be held to share resuls and discuss their implications. Third, we will develop a database and web presence to archive, share, and integrate these data, as well as workflows submitted by users to enable replication and dissemination of their methods. This community-oriented effort will establish a powerful new platform for advancing the state of the art in computer-aided drug design.

描述（由申请人提供）：尽管在学术界广泛使用了这些方法，但计算机辅助药物设计（CADD）在药物发现中的应用尚未发挥全部潜力35年。尽管现有的计算方法很有用，但预测小分子配体蛋白靶靶点的能力存在严重的局限小分子药物发现时间表的减少和潜在的有毒脱靶效应，从而降低了整体开发成本并提高了新药物的安全性。计算机辅助的药物设计界正在努力开发改进的方法，并且普遍认为，进一步的进步需要更大的公众可用性，更具吸引力和“问题”特定的蛋白质配体数据集，以挑战，改进和验证计算算法。 NIH试图通过发行RFA GM-08-008“药物对接和筛查数据资源”来解决此问题。我们提出一项提案，以建立公开可用的药物设计数据资源（D3R），以实现此RFA的目标。我们提出了三个创新的CADD社区目标。首先，我们将与我们的药物合作伙伴进行识别，策划和增强每年6-10个蛋白质数据集。这项工作将在现有的CSAR项目基础上通过快速合并其数据集以及使其学术行业关系永存。 CSAR以外的创新扩展将是制药公司更长的任期，以便进一步交换想法并使用各种工作流程，可以公开可用的工作流程进行数据测试。我们将与合同研究组织一起进行复合合成和体外生化分析，以及我们的学术合作伙伴以获取新型热力学数据。其次，我们将使用这些新数据集作为使CADD社区参与季度盲目预测练习的基础，重点关注装订模式和配体蛋白质相互作用的亲和力预测。这种盲目挑战方法已被证明在其他领域，例如蛋白质折叠。研讨会将举行，以分享重新调整并讨论其含义。第三，我们将开发一个数据库和Web的影响力来存档，共享和集成这些数据，以及用户提交的工作流程以启用其方法的复制和传播。这项面向社区的工作将建立一个强大的新平台，以推动计算机辅助药物设计中的最新水平。