The Era of AI-Driven Drug Abundance May Exacerbate Biopharma R&D Indigestion in the Short Term

Life sciences investors note that while AI is drastically lowering the barrier to molecular design and spurring a surge of drug candidates, it may actually degrade industry R&D returns over the next 3 to 5 years rather than improve efficiency. Because mainstream AI models are trained on datasets constrained by known chemical scaffolds and targets, their capabilities are primarily suited to patent workarounds and marginally differentiated follow-on drugs, rather than tackling truly undruggable novel targets. This has overcrowded pipelines for popular targets and intensified competition in clinical trial recruitment and preclinical supply chains. In the short term, the industry faces an R&D indigestion cycle reminiscent of the post-2001 genomics bubble.