Quantum Chemistry Methods for Rational Drug Design

项目摘要 一个可扩展的计算量子力学方法的非共价蛋白质配体相互作用将发展 基于“扩展的”自适应微扰理论（XSAPT）， 这是专门为大的超分子复合物设计的，并提供了一个证明的准确性， ;$1千卡/月！关于现有的最佳从头算基准。在这项工作的第一阶段，我们将加强 通过更好的并行化，XSAPT的效率将使蛋白质-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， 评估方法的准确性，并生成包括蛋白质-配体相互作用能的数据集 以及它们的组成部分：静电、空间排斥、色散、极化和电荷转移。数据集 将建立在标准的晶体结构，但也将包括非平衡结构， 以及晶体结构和其他实验数据不可用的小配体片段;后者 是基于片段的药物发现策略的代表。这些是相互作用能的挑战性案例 计算只能通过使用量子力学的预测能力来定量解决，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 这是现有的评分函数（即使是基于ML的）无法做到的。第二阶段额外工作 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），这将有利于铅的产生和铅优化药物发现，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.