Approximately 90% of drug candidates entering clinical trials fail, primarily due to inadequate pharmacokinetics and unacceptable toxicity. The 2025 OpenADMET blind challenge had 65+ teams submit predictions before experimental results were revealed. Deep learning beat classical methods for ADME prediction; classical methods remained competitive for potency.
The AI-PBPK Platform
Wang et al. at Macau University published an AI-PBPK platform predicting eight molecular properties from structure alone and feeding them into a physiologically-based PK model. Validated against 677 human PK datasets, most AUC predictions fell within 2-3x fold error of experimental data, acceptable for early-stage decision-making.
Where AI ADMET Fails
Idiosyncratic toxicity reactions depend on individual immune variability that cannot be predicted from molecular structure. Potency prediction still favors classical methods for many targets.
Related coverage: Generative AI for Small Molecule Drug Discovery | AlphaFold 3 in Drug Discovery: Where It Works and Where It Fails
Primary sources: Fischer Y et al., J Chem Inf Model 2025;65(24); Wang W et al., Clin Pharmacol Ther 2025;118(4).