Advancing predictive physical modeling through focused development of model systems to drive new modeling innovations

通过集中开发模型系统来推进预测物理建模，以推动新的建模创新

• 批准号: 10165354
• 负责人: David Lowell Mobley
• 金额: $ 23.55万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-10 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10165354
• 关键词: Address; Administrative Supplement; Adoption; Affinity; Area; Automation; Automobile Driving; Binding; Biological Models; Blinded; Chemistry; Cloud Computing; Communities; Computer software; Data; Deposition; Development; Disease; Docking; Drug Design; Drug Industry; Ensure; Failure; Free Energy; Funding; Human; Infrastructure; Institutes; Knowledge; Ligands; Link; Methodology; Methods; Modeling; Molecular; Participant; Partition Coefficient; Performance; Pharmacologic Substance; Play; Process; Property; Publications; Registries; Reproducibility; Research Personnel; Running; Science; Series; Software Tools; Solubility; Stress Tests; Structural Models; System; Techniques; Technology; Testing; Time; United States National Institutes of Health; Universities; Work; arm; base; blind; career; college; computational chemistry; computing resources; cost; crowdsourcing; data resource; design; drug development; drug discovery; experimental study; falls; innovation; insight; interoperability; lead optimization; model development; novel; personalized medicine; physical model; physical property; predictive modeling; receptor binding; repository; small molecule therapeutics; software infrastructure; targeted treatment; tool

PROJECT SUMMARY/ABSTRACT This work seeks to advance quantitative methods for biomolecular design, especially for predicting biomolecular interactions, via a focused series of community blind prediction challenges. Physical methods for predicting binding free energies, or “free energy methods”, are poised to dramatically reshape early stage drug discovery, and are already finding applications in pharmaceutical lead optimization. However, performance is unreliable, the domain of applicability is limited, and failures in pharmaceutical applications are often hard to understand and fix. On the other hand, these methods can now typically predict a variety of simple physical properties such as solvation free energies or relative solubilities, though there is still clear room for improvement in accuracy. In recent years, competitions and crowdsourcing have proven an effective model for driving innovations in diverse fields. In our field, blind prediction challenges have played a key role in driving innovations in prediction of physical properties and binding, especially in the form of the SAMPL series of challenges. Here, we will continue and extend SAMPL prediction challenges to include new physical properties, more complicated host-guest binding data, and application to biomolecular systems. Carefully selected systems and novel experimental data will provide challenges of gradually increasing complexity spanning between systems which are now tractable to those which are marginally out of reach of today's methods but still slightly simpler than those covered by the Drug Design Data Resource (D3R) series of challenges on existing pharmaceutical data. We will work with D3R to run blind challenges on the data we generate and to ensure it is designed to maximally benefit the field. In our original proposal, Aim 4 focused on using data generated in a SAMPL series of challenges, applying proven crowdsourcing-based techniques to drive the development of new methods and new understanding of the strengths and weaknesses of existing techniques. Here, we extend this work by building out software infrastructure for a fully automated component of these challenges, where workflow components can be deposited in a common registry and then linked together to automate participation in SAMPL challenges. This solves several key problems at once, and will allow innovations resulting from the SAMPL challenges to have much greater impact on the community and much more rapidly disseminate to a wide variety of applications. Users of software employed in the SAMPL challenges number in the thousands to tens of thousands, so this will have far-reaching implications for the predictive modeling community.

项目摘要/摘要 这项工作旨在推进生物分子设计的定量方法，尤其是用于预测 生物分子相互作用，通过一系列集中的社区盲目预测挑战。物理方法 预测结合的自由能或“自由能方法”被毒死以急剧重塑早期药物 发现，并且已经在药品铅优化中找到了应用。但是，性能是 不可靠的，适用性的领域是有限的，并且药物应用中的失败通常很难 理解和修复。另一方面，这些方法现在通常可以预测各种简单的物理 诸如解决自由能或相对溶解度之类的属性，尽管仍然有明确的空间 准确性的提高。近年来，竞争和众包已被证明是一个有效的模型 推动潜水场领域的创新。在我们的领域，盲目预测挑战在驾驶中发挥了关键作用 预测物理特性和结合的创新，尤其是以样本系列的形式 挑战。在这里，我们将继续并扩展样本预测挑战，以包括新的物理 属性，更复杂的宿主 - 环境结合数据以及应用于生物分子系统。 精心选择的系统和新型实验数据将为逐渐增加的挑战 现在跨越系统之间的复杂性，这些系统现在可以限制地触及的系统。 当今的方法，但仍然比药物设计数据资源（D3R）系列涵盖的方法略简单 现有药物数据的挑战。我们将与D3R合作，在我们的数据上实现盲目挑战 生成并确保其旨在最大化该领域的利益。 在我们最初的提案中，AIM 4专注于使用一系列挑战中生成的数据，应用 可靠的基于众包的技术来推动新方法的开发和新的理解 现有技术的优点和缺点。在这里，我们通过构建软件来扩展这项工作 这些挑战的完全自动化组件的基础架构可以是工作流程组件 存放在共同注册表中，然后将其链接在一起，以自动参与Sampl挑战。这 立即解决几个关键问题，并将允许由Sampl挑战产生的创新 对社区的影响更大，并且更快地传播到各种应用程序。 Sampl中使用的软件用户挑战数字数量达到数万，因此 将对预测建模社区具有深远的影响。