Existing molecules, new targets, and the algorithms trying to tell real biology from statistical coincidence.

AI is turning a decade of regulatory ambition around real-world evidence into a practical clinical development tool.

Better patient selection, not better molecules alone, decides many trial outcomes.

AI in clinical trials is reshaping patient selection, though the translational gap remains unresolved.

Central nervous system drug design lives or dies on brain penetration, and AI now predicts it well.

Every ADMET model has a confidence interval; few teams have a rule for when to act on it.

AI drug-drug interaction models are sophisticated enough to shape candidate choices, within limits.

AI toxicity models catch real safety liabilities, but off-target biology still slips past them.

Metabolic liability drops promising compounds early, and machine learning is changing how that call gets made.

Machine learning flags ADMET liabilities earlier, but knowing when to trust it still matters most.

Screening scales fast, but confirmed binding still lags far behind the predictions.

Machine learning ranks targets fast, but only genetics and bench experiments confirm they actually work.