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

项目摘要 计算化学对商业药物发现的价值现在已经确立。虚拟筛选 （包括分子对接）现在启动了大多数发现工作。分子动力学和 自由能微扰越来越多地用于通知铅精炼的后期阶段。日益增长的 基于计算结构的方法的重要性影响了所鉴定的配体的类型。 分子系统的能量完全由量子力学（QM）描述。然而，QM方程是 非常复杂，并将QM应用于与药物发现相关的现实模型， 传统上，时间表是不可能的。相反，分子相互作用的简化公式， 机械（MM），已被使用。MM的解析方程可以很容易地直接从 分子结构的坐标。然而，MM相对于QM表示受到严重的限制， 包括对某些类型的分子效应（极化、π堆积和与 金属和卤素）和不能处理拓扑结构的变化，包括键的产生/断裂。 由于后者的限制，计算时代的药物发现主要集中在非共价键上。 抑制剂的然而，共价药物具有历史意义（阿司匹林、青霉素、超过50种FDA批准的药物）。 药物总量）。人们越来越认识到，共价药物可以提供一种解决非药物问题的方法。 共价配体不能解决的问题导致了对药物共价性的兴趣的复苏。在这些目标中 特别适合于共价药物的是：在相似结合位点之间区分的药物（例如，的 激酶家族）;可导致蛋白质降解的Protec药物;以及可靶向“不可用药”的配体 例如蛋白质-蛋白质相互作用。反过来，这种认识导致了对QM方法的新兴趣。 我们最近描述了一种新的，新颖的QM实现，（第一次）允许精确的DFT/QM， 应用于大的配体/蛋白质系统，具有足够的药物发现通量。这种新方法 允许在大规模分布式计算平台上在不到一个小时的时间内进行计算， 相比之下，使用传统的QM实现需要数周或数月。这使得可以使用基于QM的 计算工具来优化共价配体-包括以前难以实现的目标，如调整 配体上的“弹头”反应基团。我们开展的后续工作进一步证明， QM改进共价结合配体的标准评分方法的能力。这使我们 开发一种方法，将简化和优化计算驱动的共价配体的过程， 特征化结果将是一个QM方法，可以可靠地集中配体优化-包括 在与现代药物发现相称的时间尺度上。

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.