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 believes AI will intensify target crowding and me-too competition against hot targets.

In light of this, I believe AI will undoubtedly exacerbate the crowding challenge in our sector – that intense competition that has brought over 100 global programs into R&D against each of the top eight targets

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.