Improved optimization of covalent ligands using a novel implementation of quantum mechanics suitable for large ligand/protein systems.

使用适用于大型配体/蛋白质系统的量子力学的新颖实现改进了共价配体的优化。

• 批准号: 10601968
• 负责人: David A Pearlman
• 金额: $ 14.86万
• 依托单位: QUANTUM SIMULATION TECHNOLOGIES, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-13 至 2024-03-12
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10601968
• 关键词: Address; Aspirin; Binding; Binding Sites; Clinical Trials; Complex; Computing Methodologies; Covalent Interaction; Data; Docking; Drug Targeting; Environment; Equation; Explosion; FDA approved; Family; Formulation; Free Energy; Geometry; Goals; Halogens; Hour; Lead; Legal patent; Ligand Binding; Ligands; Metals; Methods; Modeling; Modernization; Molecular; Molecular Structure; Nature; Penicillins; Pharmaceutical Preparations; Pharmacologic Substance; Phosphotransferases; Process; Proteins; Publishing; Quantum Mechanics; Quantum Theory; Reaction; Reliability of Results; Running; Sampling; Series; Specificity; Structure; System; Tail; Time; Triage; Validation; Work; cluster computing; computational chemistry; computational platform; computerized tools; cost; covalent bond; density; design; distributed memory; drug discovery; improved; inhibitor; innovation; interest; mechanical force; molecular dynamics; molecular mechanics; novel; novel strategies; parallelization; pre-clinical; process optimization; programs; protein degradation; protein protein interaction; small molecule; theories; timeline; tool; virtual screening

Project Summary The value of computational chemistry to commercial drug discovery is now well-established. Virtual screening (including molecular docking) now jumpstarts most discovery efforts. Tools such as molecular dynamics and free energy perturbation are increasingly used to inform the later stages of lead refinement. The growing importance of computational structure-based methods has influenced the types of ligands that are identified. The energy of a molecular system is fully described by quantum mechanics (QM). However, QM equations are extraordinarily complex, and applying QM to realistic models of relevance to drug discovery on a suitable timescale has traditionally been impossible. Instead, a simplified formulation of molecular interaction, molecular mechanics (MM), has been used. The analytic equations of MM can be easily assessed directly from the coordinates of a molecular structure. However, MM suffers severe limitations relative to the QM representation, including poor estimation of certain types of molecular effects (polarization, π-stacking, and interactions with metals and halogens) and an inability to deal with changes in topology, including bond creation/breakage. Because of this latter limitation, drug discovery in the computational era has focused largely on non-covalent inhibitors. However, covalent drugs are historically significant (aspirin, penicillin, more than 50 FDA approved drugs in total). A growing realization that covalent drugs can provide a way to address problems that non- covalent ligands cannot address has led to a resurgence in interest in drug covalency. Among the targets that are especially well suited for covalent drugs are: drugs that differentiate among similar binding sites (e.g., the Kinase family); Protec drugs that can lead to protein degradation; and ligands that can target “undruggable” targets such as protein-protein interactions. In turn, this realization has led to renewed interest in QM methods. We recently described a new, novel implementation of QM that (for the first time) allows accurate DFT/QM to be applied to large ligand/protein systems with sufficient throughput for drug discovery. This new approach allows calculations to be carried out in less than an hour on a massively distributed computing platform, as compared to weeks or months using traditional QM implementations. This makes it possible to use QM-based computational tools to optimize covalent ligands--including such previously elusive goals as tuning the “warhead” reactive group on the ligand. Subsequent work we have carried out has further demonstrated the ability of QM to improve upon standard scoring approaches for covalently-bound ligands. This has led us to develop an approach that will streamline and optimize the process of computationally-driven covalent ligand characterization. The result will be a QM approach that can reliably focus ligand optimization—including the warhead—on a timescale commensurate with modern drug discovery.

项目摘要 现在已经建立了计算化学对商业药物发现的价值。虚拟筛选 （包括分子对接）现在开始大多数发现工作。分子动力和等工具 自由能扰动越来越多地用于告知铅细化的后期阶段。成长 基于计算结构的方法的重要性影响了鉴定的配体类型。 分子系统的能量由量子力学（QM）充分描述。但是，QM方程是 非常复杂，并将QM应用于合适的药物发现的现实模型 传统上，时间尺度是不可能的。相反，简单的分子相互作用公式，分子 力学（MM）已被使用。 MM的分析方程可以很容易地直接从 分子结构的坐标。但是，相对于QM表示，MM遭受严重的限制， 包括对某些类型的分子效应的估计不佳（偏光，π堆积和与 金属和卤素）以及无法处理拓扑变化，包括创造债券/破裂。 由于这种局部限制，计算时代的药物发现主要集中在非共价上 抑制剂。但是，共价药物在历史上很重要（阿司匹林，青霉素，超过50 FDA批准 总共药物）。越来越多的认识到共价药物可以提供一种解决非问题的方法 共价配体无法解决，导致对药物共价的兴趣复兴。在目标中 特别适合共价药物的非常适合：在相似结合位点区分的药物（例如， 激酶家族）；可以导致蛋白质降解的蛋白质药物；和可以针对“不可能”的配体 诸如蛋白质蛋白质相互作用之类的靶标。反过来，这种认识导致对QM方法的兴趣重新兴趣。 我们最近描述了一种新的新型QM实现，该实现（首次）允许精确的DFT/QM到达 应用于具有足够吞吐物的大型配体/蛋白质系统。这种新方法 允许在不到一个小时的大规模分布式计算平台上进行计算，因为 与传统QM实施相比，与几周或几个月相比。这使得使用基于QM的 优化共价配体的计算工具 - 包括调整以前难以捉摸的目标 配体上的“弹头”反应群。随后我们进行的工作进一步证明了 QM改进共价结合配体的标准评分方法的能力。这导致了我们 开发一种方法可以简化和优化计算驱动的共价配体的过程 表征。结果将是一种QM方法，可以可靠地集中配体优化 - 包括 弹头 - 与现代药物发现相称的时间表。