Astex Pharmaceuticals, 436 Cambridge Science Park, Cambridge CB4 0QA, U.K.
J Med Chem. 2022 Jun 9;65(11):7476-7488. doi: 10.1021/acs.jmedchem.2c00164. Epub 2022 May 5.
Optimization of electrostatic complementarity is an important strategy in structure-based drug discovery for improving the affinity of molecules against a specific protein target. In this Miniperspective we identify examples where deliberate optimization of protein-ligand electrostatic complementarity or intramolecular electrostatic interactions gave improvements in target affinity (up to 250-fold), physicochemical properties, properties, and off-target selectivity. We also look retrospectively at a series of factor Xa inhibitors that show an almost 8000-fold range in potency that can be correlated with the calculated electrostatic potential (ESP) surfaces. Recent developments using a graph-convolutional deep neural network to rapidly generate high quality ESP surfaces have the potential to make this useful tool more accessible for a wider audience within the field of medicinal chemistry.
静电互补性的优化是基于结构的药物发现中的一个重要策略，可提高分子对特定蛋白质靶标的亲和力。在这篇小综述中，我们确定了一些例子，其中故意优化蛋白质-配体静电互补性或分子内静电相互作用提高了靶标亲和力（高达 250 倍）、物理化学性质和对非靶标选择性。我们还回顾了一系列因子 Xa 抑制剂，它们的效力范围几乎相差 8000 倍，可以与计算出的静电势能（ESP）表面相关联。最近使用图卷积深度神经网络快速生成高质量 ESP 表面的发展，有可能使这个有用的工具在药物化学领域更广泛的受众中更容易获得。
