Work

Feature-Guided Machine Learning for Studying Passive Blood–Brain Barrier Permeability to Aid Drug Discovery

Year: 2025

Type: article

Abstract: Effective prediction of blood-brain barrier (BBB) permeability remains essential for central nervous system drug development. This study evaluates multiple supervised machine learning models using a public dataset of permeable and non-permeable compounds. Random Forest models demonstrate optimal balance between accuracy and generalizability, outperforming more complex gradient boosting methods that were prone to overfitting. Feature analysis identifies NH/OH and NO group counts as key determinants of passive diffusion, with reduced hydrogen bond donor and heteroatom counts enhancing permeability. Additionally, model performance deteriorates at NH/OH count = 3, establishing this as a decision boundary where hydrogen bonding complexity disrupts reliable prediction. This study shows the non-linear structure-permeability relationships that challenge traditional descriptor-based approaches, while demonstrating that machine learning can simultaneously provide both accurate prediction and applicable insights for drug discovery applications.

Source: International Journal of Molecular Sciences

DOI: 10.3390/ijms262211228

Authors: Baining Zhu, Suwei Liu

Institutions: Phillips Exeter Academy, Northwestern University

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,499