Booth predicts AI will drive a wave of drug abundance that worsens aggregate R&D productivity over the next 3-5 years before improving it.

But here’s my prediction: AI will drive a wave of drug abundance and exacerbate the industry’s aggregate R&D productivity challenges over the next 3-5 years, rather than improve those metrics. Great new technology waves often make things worse before they make them better.

Booth argues today's AI mainly democratizes design around known targets and scaffolds, with poor predictive power beyond its training data.

But, critically, AI today is predominantly democratizing design around known targets, known drug scaffolds, and known biology. The training sets for most AI models involve the “known” drug discovery landscape, and predictive ability outside of those sets is poor

Booth notes AI is especially good at circumventing patents by designing incrementally different molecules.

Indeed, patent-busting is one of AI’s strong suits: mine a patent application, identify what is likely the “lead” drug structure or sequence, and figure out where the soft spots in the patent are to make new molecules, incrementally different and potentially better than the patented version.

Booth is skeptical AI can deliver more than 10-20% incremental improvement in clinical development.

I’m somewhat skeptical that the embedded inefficiencies and latencies of the industry’s Development machine (like “in-life” treatment periods) offer more than incremental 10-20% improvement opportunities for AI impact.

Booth advises novel biotechs to adopt more protective disclosure practices, such as filing patents later and publishing less, to guard against AI-enabled copying.

it seems clear that one needs to fundamentally change the disclosure norms in this new AI (and China) reality: file patents later, use more decoy filings, keep PR to a minimum, fewer conference posters for early programs, limit the number and depth of scientific publications, etc….