互补性(分子生物学)
化学
分子内力
静电学
药物发现
分子
静电相互作用
组合化学
纳米技术
立体化学
化学物理
生物化学
有机化学
生物
物理化学
遗传学
材料科学
作者
Benjamin D. Cons,David G. Twigg,Rajendra Kumar,Gianni Chessari
标识
DOI:10.1021/acs.jmedchem.2c00164
摘要
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, in vitro 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.
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