Machine Learning in Drug Development for Neurological Diseases: A Review of Blood Brain Barrier Permeability Prediction Models

作者：A.H.M. Nurun Nabi, Pedram Pouladvand, Litian Liu, Yining Hua, Cyrus Ayubcha · 发表于：Molecular Informatics · 年份：2025 · DOI：10.1002/minf.202400325 · 被引用次数：35 · 研究领域：Computational Drug Discovery Methods、Drug Transport and Resistance Mechanisms、Barrier Structure and Function Studies

The blood brain barrier (BBB) is an endothelial-derived structure which restricts the movement of certain molecules between the general somatic circulatory system to the central nervous system (CNS). While the BBB maintains homeostasis by regulating the molecular environment induced by cerebrovascular perfusion, it also presents significant challenges in developing therapeutics intended to act on CNS targets. Many drug development practices rely partly on extensive cell and animal models to predict, to an extent, whether prospective therapeutic molecules can cross the BBB. In interest to reduce costs and improve prediction accuracy, many propose using advanced computational modeling of BBB permeability profiles leveraging empirical data. Given the scale of growth in machine learning and deep learning, we review the most recent machine learning approaches in predicting BBB permeability.