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Comparative Machine Learning and Graph Neural Network Approaches for Blood-Brain Barrier Permeability Prediction

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

Versioned research release of the BBB permeability prediction project. This release preserves the complete source code, experimental results, documentation, figures, and reproducibility materials for the project. Contents: Phase 3: 5-fold scaffold CV classical ML (RF AUROC 0.921+/-0.024) Phase 4: Chemprop GNN ensemble (AUROC 0.935, SCAFFOLD_BALANCED) Matched H2H: RF 0.9031 vs GNN 0.8669 (Fold 0, 296 molecules) Chemical error analysis with Mann-Whitney U tests 6 publication-quality figures Full scientific manuscript and documentation ChemBERTa environment-verified but not trained All experimental results are frozen and provenance-verified. No new model training was performed No experimental CSV files were modified No model checkpoints were altered Repository presentation: README redesigned as a pharma+AI portfolio project CITATION.cff validated against CFF schema v1.2.0 Full documentation in docs/ directory This release includes the corrected CITATION.cff file for proper Zenodo DOI minting.

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