Quantum Chemistry Methods for Rational Drug Design

项目摘要 将开发一种可扩展的非共价蛋白质-配体相互作用的计算量子力学方法 基于“扩展的”对称适应微扰理论(XSAPT)，一种立方标度、基于片段的方法 这是专门为大型超分子络合物设计的，它提供了证明的准确性 ；1大卡/月！关于最好的从头算基准。在这项工作的第一阶段，我们将加强 XSAPT通过更好的并行化来提高效率，这将使常规应用于蛋白质-配体模型成为可能 包含300多个原子，仅使用有限的计算资源。将开发一个自举程序来 评估该方法的准确性，将生成包括蛋白质-配体相互作用能量的数据集 及其组成部分：静电学、空间斥力、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具有挑战性的案例 只能通过使用量子力学的预测能力，而不是通过 经验计分函数或通过对实验数据进行拟合。在第二阶段，该数据集将用于训练一台机器 学习(ML)模型，能够可靠和定量地对配体结合能进行排序 这是现有的计分函数(即使是基于ML的计分函数)所不能做到的。额外的第二阶段工程 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)，这将促进药物发现中的线索产生和线索优化，基于定量 从头算能量学。

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.