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abinittio/README.md

Typing SVG


Python PyTorch TypeScript RDKit Next.js


What I work on

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.


ADMET & Quantum

Quantum-enhanced GNN with 34-dimensional features from 3D conformers — HOMO/LUMO, Fukui indices, chemical hardness.

GATv2 + TransformerConv · Pretrained on 320k ZINC molecules

Monoamine transporter (DAT/NET/SERT) substrate vs blocker classification with stereochemistry-aware GNN encoding.

0.968 AUC · Stereo-aware · DAT · NET · SERT

Cardiac safety prediction via hERG channel inhibition modelling. Focal loss for class imbalance, K-fold ensembling with test-time augmentation.

Trained on 7,000+ ChEMBL compounds

Multi-task GNN for drug-drug interaction screening across 5 cytochrome P450 enzymes (1A2, 2C9, 2C19, 2D6, 3A4).

Shared representation · Task-specific heads

Applied Materials Chemistry

Modified-release dosage form design engine — BCS classification, Noyes-Whitney GI dissolution, multi-model release kinetics, and PDF report generation.

Higuchi · Korsmeyer-Peppas · Zero-order · Bimodal IR+ER · Streamlit UI

Precision agriculture system — soil analysis, Monte Carlo yield prediction, and fertiliser recommendations.

244,000+ soil samples · Real-time market data · Uncertainty quantification

Physics-based dental composite property predictor — rule of mixtures density engine with Bayesian Monte Carlo uncertainty quantification over shrinkage and void content.

Rule of mixtures · Noyes-Whitney · Monte Carlo · Streamlit UI

ML for Drug Discovery

Full ADMET prediction platform — monoamine transporter substrate vs blocker classification with stereochemistry-aware GNN.

0.974 AUC · GAT + GIN · Deployed on HuggingFace Spaces

Drug abuse risk classification combining MAT activity with SMARTS-based structural pattern recognition and SAR pharmacology rules.

Validated on 80 DEA-scheduled compounds · HIGH/MODERATE/LOW risk tiers

Blood-Brain Barrier permeability predictor — hybrid GAT → GCN → GraphSAGE with focal loss and stereo-aware encoding.

0.9612 AUC on 7,807-compound external validation · Outperforms ADMETlab 2.0

Simulation & PK/PD

PK/PD simulation engine solving coupled ODEs for prodrug systems with saturable enzymatic conversion.

RK4 · Michaelis-Menten · Sigmoid Emax · Bayesian personalisation

Technical stack

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

Popular repositories Loading

  1. BBB-Permeability-GNN BBB-Permeability-GNN Public

    Interpretable GNN for Blood-Brain Barrier prediction with molecular generation and structure-activity analysis.

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  2. StereoAwareGNN StereoAwareGNN Public

    Blood-Brain Barrier permeability predictor using hybrid GAT-GCN-GraphSAGE architecture. 0.9612 AUC on 7,807-compound external validation.

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  3. SoilIntelligence SoilIntelligence Public

    Predicts quality of soil for any coordinate in Africa and recommends fertiliser to maximise yield

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  4. Insilico-Drug-Discovery-Toolkit Insilico-Drug-Discovery-Toolkit Public

    Comprehensive ADMET prediction platform: monoamine transporter classification (0.974 AUC), abuse liability, hERG cardiotoxicity, CYP450 metabolism.

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  5. abinittio abinittio Public

  6. DoseTrack DoseTrack Public

    Pharmacokinetic/Pharmacodynamic simulation engine with RK4 ODE solver, Michaelis-Menten kinetics, and adaptive personalisation. Built with Next.js and TypeScript.

    TypeScript