溶剂化
范畴变量
药物发现
生物信息学
渗透(战争)
等离子体
统计物理学
物理
化学
计算机科学
数学
分子
生物信息学
机器学习
生物
核物理学
运筹学
量子力学
生物化学
基因
作者
Morgan Lawrenz,Mats Svensson,Mitsunori Kato,Karen H. Dingley,Jackson Chief Elk,Zhe Nie,Yefen Zou,Zachary Kaplan,H. Rachel Lagiakos,Hideyuki Igawa,Éric Therrien
标识
DOI:10.1021/acs.jcim.3c00150
摘要
The blood-brain barrier (BBB) plays a critical role in preventing harmful endogenous and exogenous substances from penetrating the brain. Optimal brain penetration of small-molecule central nervous system (CNS) drugs is characterized by a high unbound brain/plasma ratio (Kp,uu). While various medicinal chemistry strategies and in silico models have been reported to improve BBB penetration, they have limited application in predicting Kp,uu directly. We describe a physics-based computational approach, a quantum mechanics (QM)-based energy of solvation (E-sol), to predict Kp,uu. Prospective application of this method in internal CNS drug discovery programs highlights the utility and accuracy of this new method, which showed a categorical accuracy of 79% and an R2 of 0.61 from a linear regression model.
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