I develop graph neural networks and numerical simulation tools that encode real chemistry — stereochemistry, quantum descriptors, pharmacokinetics — into predictive models. My focus is bridging the gap between physical chemistry and modern ML, where most tools ignore 3D molecular structure entirely.
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Full ADMET prediction platform — monoamine transporter substrate vs blocker classification with stereochemistry-aware GNN.
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Drug abuse risk classification combining MAT activity with SMARTS-based structural pattern recognition and SAR pharmacology rules. Validated on |
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Cardiac safety prediction via hERG channel inhibition modelling. Focal loss for class imbalance, K-fold ensembling with test-time augmentation. Trained on |
Multi-task GNN for drug-drug interaction screening across 5 cytochrome P450 enzymes (1A2, 2C9, 2C19, 2D6, 3A4). Shared representation · Task-specific heads |
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Blood-Brain Barrier permeability predictor — hybrid GAT → GCN → GraphSAGE with focal loss and stereo-aware encoding.
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Quantum-enhanced GNN with 34-dimensional features from 3D conformers — HOMO/LUMO, Fukui indices, chemical hardness. GATv2 + TransformerConv · Pretrained on |
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PK/PD simulation engine solving coupled ODEs for prodrug systems with saturable enzymatic conversion. RK4 · Michaelis-Menten · Sigmoid Emax · Bayesian personalisation |
Precision agriculture system — soil analysis, Monte Carlo yield prediction, and fertiliser recommendations.
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Molecular ML PyTorch · PyTorch Geometric · RDKit · DGL
Quantum descriptors HOMO/LUMO · Fukui indices · Gasteiger charges · ETKDG conformers
Numerical methods RK4 · Michaelis-Menten · Hill equation · Sigmoid Emax
Languages Python · TypeScript
Frameworks Streamlit · Gradio · Next.js · React
Deployment HuggingFace Spaces · Vercel