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

基本信息

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
项目类别：
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

项目摘要

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).

项目摘要我们的总体目标是提供可靠，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A的结构与某种疾病有关的蛋白质结合的药物分子。通常可以使用计算机工具和该过程包括两个步骤，即识别并引导优化。以后的步骤需要高精度，并通过计算来提出使用酒精方法和分子力学（MM）的相对结合自由能（RBFE）力场。不幸的是，由于MM力场的缺陷，预测使用毒品 SBDD过程有时不可靠，仅在药物的后期才实现涉及实验研究甚至临床试验的发现过程。为了解决这个问题，我们将创建一个名为“自动化力的”的新颖，灵活和用户友好的计算基础架构现场开发人员和优化器（AFFDO）将使科学家能够快速产生高质量通过高通量从头开始计算培训数据集，并将其转换为快速和然后可以用于RBFE计算的准确模型。我们将设计商业广告质量代码并将其部署在现有的基于网络的，用户友好的药物开发平台上这在工业社区（OpenEye的Orion平台）中广受欢迎。