Computational Infrastructure for Automated Force Field Development and Optimization

项目摘要 我们的首要目标是提供可靠和高效的工具，可用于结构 基于药物发现(SBDD)。SBDD的一个重要组成部分是预测一个 与某种疾病相关的蛋白质结合的药物分子。这通常是通过使用 计算机工具和过程由两个步骤组成，即命中识别和引导 优化。后一步需要很高的精度，目前是通过计算实现的 炼金术和分子力学方法计算相对结合自由能(RBFE) 力场。不幸的是，由于MM力场的缺陷，预测候选药物使用 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 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 将创建一个新的、灵活的、用户友好的计算基础设施，名为Automated Force 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，然后可用于RBFE计算。我们将策划一个商业广告 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 这在工业界(OpenEye的猎户座平台)中很受欢迎。

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).