When a drug fails a clinical trial, nobody blames the chemist who designed it. The molecule simply joins the 90% of candidates that never reach patients. But when AI-designed drugs fail, the headline becomes The AI Drug Discovery Lie.
That asymmetry is unfair, but it points to a real question. AI is genuinely transforming the top of the drug discovery funnel: Insilico Medicine, Recursion, and Isomorphic Labs are each compressing years of work into months - through generative chemistry, automated phenomics, and structural prediction. The intuition is that more candidates should mean more successful drugs. History suggests otherwise.
Drug development has long been governed by Eroom's Law - the observation that drug development has become less efficient over time, with fewer new drugs approved per dollar spent despite major technological advances. Take combinatorial chemistry in the 1980s, which enabled simultaneous synthesis of thousands of compounds, or high-throughput screening in the early 1990s, which allowed robotic testing at industrial scale. Both expanded the top of the discovery funnel and accelerated early-stage research, but neither fundamentally changed downstream success rates.
Today’s AI drug discovery models are trained on large, heterogeneous datasets spanning molecular chemistry, structural biology, omics, imaging, and literature-derived data, much of it generated through decades of preclinical research. That lets models optimize compounds against experimentally defined endpoints. It does not close the gap between those endpoints and how a molecule actually behaves in a human body.
The track record so far reflects that gap. BenevolentAI’s BEN-2293, Recursion’s REC-994, and Verge Genomics’ VRG50635 all reached human testing. None has shown enough clinical efficacy to become an approved therapy.
Insilico Medicine’s rentosertib is the strongest signal yet that this can work. The AI system identified both the biological target and the molecule for idiopathic pulmonary fibrosis - a disease with no cure and a median survival of three to four years. In a Phase IIa study, patients on rentosertib showed a mean improvement in lung function of +98.4 mL, while placebo patients declined. In a disease that typically only gets worse, that’s a reversal, not just a slowdown. Biomarker analysis further supported the predicted target, reinforcing that the underlying biological hypothesis - not just the chemistry - was correct. If Phase III succeeds, rentosertib would be one of the first proof points that AI can improve not just the speed of drug discovery, but the quality of what comes out of it.
But one data point doesn’t remove the central constraint in drug development: even a perfect molecule-design engine still has to survive the slow, capital-intensive process of proving it works in humans.
Formation Bio argues the real constraint isn’t discovery at all - it’s clinical development, where promising drugs compete for scarce trial capacity. Under this view, faster discovery doesn’t remove the bottleneck. It just shifts it forward and lengthens the queue waiting to get into the clinic.
That reframing is why some of the more interesting companies right now are attacking the bottom of the funnel instead of the top. Formation Bio acquires stalled or clinical-stage drugs and accelerates them with an AI-native development platform - it fast-tracked and out-licensed gusacitinib to Sanofi in just 2.5 years. Trially matches and enrolls patients into trials directly from unstructured medical records. Parallel Bio pushes even further upstream, using human immune organoids to generate more predictive preclinical evidence before a drug ever reaches a patient.
Seen this way, the biggest opportunity in AI drug discovery isn’t generating more molecules. It’s generating more human-relevant evidence, earlier - because evidence, not volume, is what the funnel has always been short on.