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)：计算机辅助药物设计(CADD)在药物发现中的应用尚未充分发挥其潜力，尽管这些方法在学术界和制药行业得到了广泛应用，并且在过去35年中CPU速度取得了巨大进步。虽然现有的计算方法是有用的，但在预测小分子配体-蛋白质靶标相互作用的能力方面存在严重限制。人们认识到，如果这些障碍能够被克服，准确预测这些相互作用的能力将通过减少小分子药物发现时间线和潜在的毒性靶外效应而产生戏剧性和积极的结果，从而降低总体开发成本并增加新药的安全性。计算机辅助药物设计界正在努力开发改进的方法，并普遍同意，进一步的进展需要更多地向公众提供高质量、令人信服和“有问题”的特定蛋白质配基数据集，以挑战、改进和验证计算算法。美国国家卫生研究院试图通过发布RFA GM-08-008《药物对接和筛选数据资源》来解决这一问题。我们提交了一份提案，建议建立一个公开可用的药物设计数据资源(D3R)，以满足本RFA的目标。我们提出了三个面向社区的创新CADD目标。首先，我们将与我们的制药合作伙伴合作，每年识别、管理和增强6-10个蛋白质配体数据集。这项工作将建立在现有的CSAR项目的基础上，既通过快速纳入他们的数据集，又通过保持他们的学术与行业关系。CSAR之外的一个创新扩展将是在制药公司任职更长时间，允许通过各种工作流程进一步交流想法和测试数据，这些工作流程可以公开提供。我们将聘请合同研究机构进行化合物合成和体外生化分析，并聘请我们的学术合作伙伴提供新的热力学数据。其次，我们将使用这些新的数据集作为基础，让CADD社区参与季度盲预测练习，重点是配体-蛋白质相互作用的结合模式和亲和力预测。这种盲目挑战的方法在其他领域被证明是非常有效的，例如蛋白质折叠。将举办研讨会，分享结果并讨论其影响。第三，我们将开发一个数据库和网络存在，以存档、共享和整合这些数据，以及用户提交的工作流程，以便能够复制和传播他们的方法。这一面向社区的努力将为推进计算机辅助药物设计的最先进水平建立一个强大的新平台。