Computational Infrastructure for Automated Force Field Development and Optimization

用于自动力场开发和优化的计算基础设施

• 批准号: 10699200
• 负责人: Madushanka Manathunga
• 金额: $ 27.54万
• 依托单位: ATTMOS INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2024-03-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10699200
• 关键词: Address; Adoption; Binding; Binding Proteins; Biochemical; Biotechnology; Clinical Trials; Code; Collaborations; Communities; Complex; Computer software; Computers; Computing Methodologies; Data; Data Set; Databases; Development; Disease; Engineering; Ensure; Feedback; Free Energy; Generations; Goals; Industrialization; Infrastructure; Intuition; Letters; Ligands; Literature; Machine Learning; Marketing; Measures; Methodology; Methods; Modeling; Names; Online Systems; Performance; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Play; Positioning Attribute; Process; Proteins; Publications; Publishing; Reporting; Research; Role; Running; Scanning; Scientist; Series; Side; Small Business Innovation Research Grant; Software Tools; Specificity; Structure; Surveys; System; Technology; Testing; Thermodynamics; Training; Validation; Work; cloud based; computer infrastructure; computer program; cost; drug candidate; drug development; drug discovery; drug-like compound; experimental study; flexibility; improved; in silico; interest; intermolecular interaction; lead optimization; mechanical force; molecular dynamics; molecular mechanics; novel; novel therapeutics; phase 2 study; prospective; simulation; tool; user-friendly

Project Abstract Our overarching goal is to provide reliable and efficient tools that can be used in structure based drug discovery (SBDD). One crucial component of SBDD is to predict the structure of a drug molecule that binds to a protein involved in a certain disease. This is usually achieved using computer tools and the process consists of two steps, namely hit identification and lead optimization. The latter step requires high accuracy and is presently achieved by computing relative binding free energies (RBFE) using alchemical methods and molecular mechanics (MM) forcefields. Unfortunately, due to deficiencies in MM forcefields, predicted drug candidates using the SBDD process are sometimes unreliable, which is only realized at the later stages of the drug discovery process involving experimental studies or even clinical trials. To address this issue, we will create a novel, flexible and user-friendly computational infrastructure named Automated Force Field Developer and Optimizer (AFFDO) that will allow scientists to quickly generate high-quality training datasets through high-throughput ab initio calculations and transform them into fast and accurate models which can then be used for RBFE calculations. We will engineer a commercial quality code and deploy it on an existing web-based, user-friendly, drug development platform that is widely popular among the industrial community (OpenEye’s Orion platform).

项目摘要 我们的首要目标是提供可靠和有效的工具，可用于结构 基于药物发现（SBDD）。SBDD的一个关键组成部分是预测一个 与某种疾病有关的蛋白质结合的药物分子。这通常是通过使用 计算机工具和过程包括两个步骤，即命中识别和引导 优化.后一步需要高精度，目前通过计算来实现。 相对结合自由能（RBFE），使用炼金术方法和分子力学（MM） 力场不幸的是，由于MM力场的缺陷，预测的候选药物使用 SBDD过程有时是不可靠的，这只有在药物的后期阶段才能实现 发现过程涉及实验研究甚至临床试验。为了解决这个问题，我们 将创建一个新颖、灵活和用户友好的计算基础设施，名为自动化部队 现场开发和优化器（AFFDO），使科学家能够快速生成高质量的 通过高通量从头计算训练数据集，并将其转换为快速和 精确的模型，然后可以用于RBFE计算。我们会策划一个广告 质量代码，并将其部署在现有的基于Web的，用户友好的药物开发平台上 在工业界广泛流行的OpenEye平台（OpenEye的Orion平台）。