bbbp

Quantum Kernel Machine Learning for Drug Design A rigorous, end-to-end Qiskit implementation of quantum kernel SVMs for predicting blood-brain barrier permeability (BBBP) — a core ADMET property in CNS drug discovery — with three controlled experiments that actually test whether the quantum part is doing anything useful.

GNN + DMPNN ensemble with MC Dropout for multi-task molecular toxicity and activity prediction across 9 datasets · Random Forest fingerprint models · FastAPI web UI with 3D viewer

Benchmarking classical, graph, and quantum machine learning models for molecular property prediction on BACE, BBBP, and ClinTox.

Molecular property prediction on BBBP using Mistral-7B. Features Nitrogen valence sanitization and property-aware prompting (MW/TPSA). Achieved 0.75+ ROC-AUC on scaffold splits for DeepChem GSoC '26.

Machine learning model for predicting blood-brain barrier permeability using RDKit molecular fingerprints, physicochemical descriptors, and XGBoost.