ArticleInternational journal of molecular sciences2025
Feature-Guided Machine Learning for Studying Passive Blood-Brain Barrier Permeability to Aid Drug Discovery.
Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- DeepBBB: A Data-Composition-Aware Graph Screening Workflow for BBB-Focused CNS Library Construction and Prospective PAMPA-BBB Evaluation.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Engineering Nanocarriers for Dopamine Stabilization and Targeted Brain Delivery: Mechanisms, Approaches and Translational Challenges.International journal of nanomedicine · 2026Review
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Authors and funding
2 authors.
Funding
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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.
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Registered trials
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