Schrödinger Inc., San Diego, California 92122, United States.
Schrödinger Inc., New York, New York 10036, United States.
J Chem Inf Model. 2023 Jun 26;63(12):3786-3798. doi: 10.1021/acs.jcim.3c00150. Epub 2023 Jun 2.
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 (K). While various medicinal chemistry strategies and models have been reported to improve BBB penetration, they have limited application in predicting K directly. We describe a physics-based computational approach, a quantum mechanics (QM)-based energy of solvation (E-sol), to predict K. 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 of 0.61 from a linear regression model.
血脑屏障 (BBB) 在防止有害的内源性和外源性物质穿透大脑方面起着至关重要的作用。小分子中枢神经系统 (CNS) 药物的最佳脑穿透特性表现为高未结合的脑/血浆比 (K)。虽然已经报道了各种药物化学策略和模型来改善 BBB 穿透性,但它们在直接预测 K 方面的应用有限。我们描述了一种基于物理的计算方法,即基于量子力学 (QM) 的溶剂化能 (E-sol),以预测 K。该方法在内部中枢神经系统药物发现计划中的预期应用突出了这种新方法的实用性和准确性,该方法从线性回归模型中显示出 79%的分类准确性和 0.61 的 r 平方值。