Articles, Explainers, Milestones

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.

Decades of genomic and clinical research were not conducted equally across populations. AI trained on that history inherits it, which raises a real question: will AI-discovered medicines work equally well for everyone?

There are vastly more possible drug-like molecules than any chemist could ever screen. Here is how generative models are actually being used to search that space, and where the approach still runs into hard limits.

No single rulebook governs artificial intelligence in drug development. Instead, three different regulators are moving at three different speeds, and knowing exactly where each one stands is now part of the job.

FDA's paper trail on artificial intelligence is longer and more scattered across centers than most bench scientists realize. Here is what each document actually says, and which ones apply to drugs rather than devices.

Patent law was not written with generative models in mind. As AI becomes central to drug discovery, questions about ownership, inventorship, and data sharing are being answered in real time, not always consistently.

A model that says a molecule scores 0.87 has not told a chemist anything actionable. Explainability is what turns a score into a design decision, and it is harder to get right than it sounds.