Quantum Chemistry Methods for Rational Drug Design

合理药物设计的量子化学方法

• 批准号: 10697148
• 负责人: Xintian Feng
• 金额: $ 24.79万
• 依托单位: Q-CHEM, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-15 至 2024-11-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10697148
• 关键词: Address; Affinity; Amino Acids; Antidotes; Benchmarking; Binding; Binding Sites; Biological Assay; Biological Models; Charge; Chemicals; Complex; Computer Assisted; Data; Data Set; Development; Docking; Drug Design; Electrostatics; Eligibility Determination; Exhibits; Foundations; Free Energy; Generations; Goals; Lead; Ligand Binding; Ligands; Machine Learning; Methods; Modeling; Molecular; Molecular Weight; Oral; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Physics; Procedures; Process; Proteins; Protocols documentation; Quantum Mechanics; Resources; Structure; TRANCE protein; Testing; Time; Training; Water; Work; candidate identification; computing resources; cost; density; design; drug candidate; drug development; drug discovery; experimental study; flexibility; high throughput screening; in silico; lead optimization; lipophilicity; machine learning model; parallelization; perturbation theory; quantum chemistry; quantum computing; screening; small molecule; theories; therapeutic target; tool; virtual; virtual screening

Project Summary A scalable computational quantum mechanics method for non-covalent protein-ligand interactions will be developed based on "extended" symmetry-adapted perturbation theory (XSAPT), a cubic-scaling, fragment-based approach that is specifically designed for large supramolecular complexes, and which affords a demonstrated accuracy of ;$ 1 kcal/mo! with respect to the best-available ab initio benchmarks. In Phase I of this work, we will enhance the efficiency of XSAPT via better parallelization that will enable routine application to protein-ligand models containing 300+ atoms, using only modest computational resources. A bootstrap procedure will be developed to assess the accuracy of the method and a data set will be generated that includes protein-ligand interaction energies and their components: electrostatics, steric repulsion, dispersion, polarization, and charge transfer. The data set will build upon standard ones derived from crystal structures but will also include nonequilibrium structures as well as small ligand fragments for which crystal structures and other experimental data are not available; the latter are representative of fragment-based drug discovery strategies. These are challenging cases for interaction energy computations that can only be addressed quantitatively by using the predictive power of quantum mechanics, not by empirical scoring functions or by fits to experimental data. In Phase II, this data set will be used to train a machine learning (ML) model that is capable of ranking-ordering ligand binding energies in a reliable and quantitative way, something that existing scoring functions ( even those based on ML) cannot do. Additional Phase II work will integrate the ML-XSAPT scoring function into virtual drug-discovery workflows (including flexible docking protocols), which will facilitate both lead generation and lead optimization in drug discovery, based on quantitative ab initio energetics.

项目概要 将开发一种用于非共价蛋白质-配体相互作用的可扩展计算量子力学方法 基于“扩展”对称适应扰动理论 (XSAPT)，这是一种立方尺度、基于片段的方法 专为大型超分子复合物设计，并提供了经过验证的准确度 ；$1 kcal/月！关于最佳可用的从头算基准。在这项工作的第一阶段，我们将加强 通过更好的并行化提高 XSAPT 的效率，这将使蛋白质配体模型的常规应用成为可能 包含 300 多个原子，仅使用适度的计算资源。将开发一个引导程序 评估该方法的准确性，并将生成包含蛋白质-配体相互作用能量的数据集 及其组成部分：静电、空间排斥、色散、极化和电荷转移。数据集 将建立在源自晶体结构的标准结构的基础上，但也将包括非平衡结构，如 以及无法获得晶体结构和其他实验数据的小配体片段；后者 是基于片段的药物发现策略的代表。这些都是相互作用能的具有挑战性的案例 只能通过使用量子力学的预测能力来定量解决的计算，而不是通过 经验评分函数或通过拟合实验数据。在第二阶段，该数据集将用于训练机器 学习（ML）模型，能够以可靠和定量的方式对配体结合能进行排序 现有的评分函数（甚至基于机器学习的评分函数）无法做到这一点。额外的第二阶段工作 将ML-XSAPT评分功能集成到虚拟药物发现工作流程中（包括灵活对接 协议），这将促进基于定量的药物发现中的先导化合物生成和先导化合物优化 从头算能量学。