Resource for Structure-based Computational Drug Discovery and Design (RSD3)

基于结构的计算药物发现和设计资源 (RSD3)

• 批准号: 10707044
• 负责人: Stefano Forli
• 金额: $ 102.33万
• 依托单位: SCRIPPS RESEARCH INSTITUTE, THE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-21 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707044
• 关键词: Address; Award; Biological; Biomedical Computing; Clinical; Cloud Computing; Code; Communities; Computer Hardware; Computer software; Computing Methodologies; Data; Development; Docking; Documentation; Drug Design; Drug Receptors; Ecosystem; Education; Educational process of instructing; Educational workshop; Ensure; Funding; Goals; Grant; Growth; Individual; Intuition; Ligands; Maintenance; Metadata; Methods; Modeling; Modernization; NIH Program Announcements; Operating System; Peer Review; Periodicals; Persons; Pharmaceutical Preparations; Physicians; Positioning Attribute; Process; Pythons; Rationalization; Reproducibility; Research; Research Personnel; Resources; Review Literature; Running; Software Engineering; Software Tools; Structure; Techniques; Technology; Teleconferences; Testing; Training; Update; Work; Writing; algorithm development; base; computational pipelines; computing resources; crowdsourcing; design; drug discovery; file format; graphical user interface; improved; interest; interoperability; migration; molecular dynamics; novel therapeutics; open source; programs; scale up; skills; small molecule; small molecule libraries; software development; success; symposium; tool; usability; video chat; virtual screening; webinar

Project summary Automated docking is a computational method that is now routinely used for identifying small molecules that have the potential to be new drugs. Over the past three decades we have developed docking methods and our docking software AutoDock is not the most cited docking software with over 40,000 citations in the peer reviewed literature. As such, it has become an important resource for the community. We seek to create a national resource that will allow us to maintain and modernize this software to adapt to evolving hardware platforms and operating systems, to keep the software up to date and relevant by incorporating the latest algorithmic developments, and to support its large user community. In parallel with these efforts, an important goal of the proposed Resource is to chart a path toward a self-sustained software ecosystem where contributors from around the world will contribute to maintain and further develop this software after the lifetime of this award. This goal has been achieved by other successful open source projects such as Debian and Python, and the written interest of many colleagues bolster our confidence that the AutoDock software too can reach this goal. To this end we propose three specific aims. Our first aim is to maintain and modernize the software code. This critical work is needed for the software to remain functional and able to address the evolving needs of the community. As we overhaul the software, we will leverage newer toolkits for generating modern and intuitive graphical user interfaces. These interfaces enable researchers, such as clinical physicians or chemists with limited computational skills for instance, to use docking to better understand the mechanism of action of a drug and optimize it. Our second aim is concerned with making our software tools interoperable with other important modeling software tools such as molecular dynamics for instance. Not only does this augment the potential of docking to lead to novel therapeutic molecules, but it also serves our goal to create a community of developers who will ultimately maintain this software ecosystem. Finally our third and final aim is about supporting the large user community, growing it and ensuring that the software is easily discovered, obtained and installed. We have a long track record of developing open source software promoting best practices in software engineering, and making these tools usable, useful, and available to the community. Likewise we have supported and grown our user community for many years. This puts us in a unique position to create the proposed research which, if funded, will allow us to convert this valuable software code into a community supported software ecosystem ensuring that this software will continue to support the design of novel therapeutic molecules beyond the lifetime of this award.

项目摘要 自动对接是一种计算方法，现在通常用于识别小分子， 有潜力成为新药。在过去的三十年里，我们开发了对接方法， 我们的对接软件AutoDock不是被引用最多的对接软件，在同行中有超过40，000次引用。 审查文献。因此，它已成为社区的重要资源。我们致力于创造一个 这将使我们能够维护和现代化这一软件，以适应不断发展的硬件 平台和操作系统，通过整合最新的 算法开发，并支持其庞大的用户社区。在作出这些努力的同时， 建议的资源的目标是绘制一条通往自我维持的软件生态系统的道路， 来自世界各地的贡献者将有助于维护和进一步开发这个软件后， 这个奖项的终身。其他成功的开源项目如Debian已经实现了这一目标 和Python，以及许多同事的书面兴趣增强了我们的信心，AutoDock软件也 才能达到这个目标。为此，我们提出三个具体目标。我们的第一个目标是保持和现代化 软件代码这一关键工作是软件保持功能并能够解决 社区的发展需求。当我们对软件进行全面检查时，我们将利用更新的工具包来生成 现代和直观的图形用户界面。这些接口使研究人员，如临床 例如，计算能力有限的医生或化学家，使用对接来更好地理解 药物的作用机制并优化它。我们的第二个目标是制作我们的软件工具 可与其他重要的建模软件工具（例如分子动力学）互操作。不仅 这是否增加了对接产生新的治疗分子的潜力，但它也符合我们的目标 创建一个开发者社区，他们将最终维护这个软件生态系统。最后，第三个 最终目标是支持大型用户社区，使其不断发展，并确保软件易于 发现，获得和安装。 我们在开发开源软件方面有着悠久的历史， 工程，并使这些工具可用，有用，并提供给社区。同样， 多年来一直支持和发展我们的用户社区。这使我们处于一个独特的位置， 如果得到资助，我们将能够将这些有价值的软件代码转化为一个社区， 支持的软件生态系统，确保该软件将继续支持新的设计 治疗性分子的研究。