AN OPEN RESOURCE TO ADVANCE 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中得到广泛使用，并且在过去35年中CPU速度取得了巨大进步。尽管现有的计算方法是有用的，但是在预测小分子配体-蛋白质靶相互作用的能力方面存在严重的限制。认识到如果可以克服这些障碍，则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表和潜在的毒性脱靶效应而具有显著和积极的结果，从而降低总的开发成本并增加新药的安全性。计算机辅助药物设计社区正在努力开发改进的方法，并普遍认为，进一步的进展需要更大的高质量，引人注目的和“问题”特定的蛋白质配体数据集的公共可用性，以挑战，改进和验证计算算法。NIH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，以建立一个公开的药物设计数据资源（D3 R），以满足本RFA的目标。我们提出了三个创新的CADD社区为导向的目标。首先，我们将与我们的制药合作伙伴合作，每年识别，策划和增强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，通过快速整合他们的数据集和永久保持他们的学术-行业关系。CSAR之外的一个创新扩展将是制药公司的更长任期，以允许进一步交换想法和测试各种工作流程的数据，这些工作流程可以公开提供。我们将与合同研究组织合作进行化合物合成和体外生化分析，并与我们的学术合作伙伴合作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CADD社区参与季度盲预测练习的基础，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已被证明在其他领域，如蛋白质折叠非常富有成效。将举办讲习班，分享成果并讨论其影响。第三，我们将开发一个数据库和网络，以存档、共享和整合这些数据，以及用户提交的工作流程，以便复制和传播他们的方法。这一面向社区的努力将建立一个强大的新平台，以推进计算机辅助药物设计的最新发展。

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.