ML MODEL FOR DRUG TOXICITY

⚗️ How I Built ML Models to Predict Drug Toxicity Before Synthesis
40% of drugs fail clinically due to ADMET issues discovered too late. I developed an ML pipeline that predicts toxicity from molecular structure—enabling smarter synthesis decisions.

THE PROBLEM 🎯 Traditional: Synthesise → Test → 60% fail ADMET. Each compound: $10K-50K to make and test. Goal: Predict Absorption, Distribution, Metabolism, Excretion, Toxicity computationally

MY ML PIPELINE
1. PROBLEM DEFINITION 15 ADMET endpoints: hERG cardiotoxicity, hepatotoxicity, BBB penetration, CYP450 inhibition, solubility, permeability, clearance, half-life. Target: >80% accuracy, 50% lab reduction

2. DATA COLLECTION 150K molecules (ChEMBL, PubChem, ToxCast), 2.3M ADMET measurements, 70/15/15 split

3. MOLECULAR FEATURIZATION Morgan fingerprints (2048-bit), RDKit descriptors (LogP, TPSA, MW), graph representations (atoms=nodes, bonds=edges), 200+ properties

4. MODEL ARCHITECTURE Ensemble: Random Forest + XGBoost + Graph Neural Networks (Chemprop) GNNs capture substructures, stereochemistry, spatial patterns

5. TRAINING 5-fold CV, Bayesian optimisation, PyTorch on A100, 2-8 hours/endpoint

6. PERFORMANCE hERG: 94% AUC, 89% accuracy | Hepatotox: 90% AUC | BBB: 95% AUC | Solubility: R²=0.82 | Prospective: 82%

7. DEPLOYMENT FastAPI + Docker, <2 sec predictions, batch 1K in 3 min, uncertainty estimates

8. MLOPS Monthly retraining (+500 compounds), MLflow versioning, drift detection, 76%→88% recovery

THE IMPACT 📊
Real Project: 200 designed analogues → ML flagged 43 high-risk → Team synthesised 157 "safe" → 41 of 43 flagged DID fail tests

Outcomes: ✅ 91% precision catching failures ✅ 60% lab reduction ✅ 4 months → 6 weeks ✅ Zero late-stage surprises (vs 25% historical) ✅ 15K+ virtual compounds evaluated in 2 months

SKILLS: ✅ Graph Neural Networks (Chemprop) ✅ Cheminformatics (RDKit, SMILES) ✅ PyTorch, XGBoost, ensemble methods ✅ MLOps (versioning, drift detection) ✅ FastAPI, Docker deployment ✅ Uncertainty quantification ✅ ADMET domain expertise

THE TAKEAWAY: ML transforms drug discovery from trial-and-error to intelligent prediction—optimising safety and efficacy while accelerating timelines.

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