Machine Learning Models for Understanding Blood-Brain Barrier Integrity and Transport Mechanisms

Abstract

The blood-brain barrier (BBB) is a pivotal physiological barrier regulating molecular exchange between the bloodstream and the central nervous system. Understanding BBB integrity and transport mechanisms is crucial for elucidating neurological disorders and improving drug delivery to the brain. This research employs machine learning models to comprehend BBB dynamics. Utilizing diverse datasets containing molecular descriptors and BBB permeability measurements, various machine learning algorithms are applied to construct predictive models. These models, trained on comprehensive in vitro and in vivo data, offer robust predictions while interpretable techniques unveil molecular determinants influencing BBB permeability. This study not only provides predictive insights into BBB function but also enhances our understanding of its complex mechanisms, potentially guiding therapeutic strategies for neurological disorders and advancing neuroscience and drug development realms.