This post expands on a talk I gave at the 5 Prime Sciences Symposium at the annual meeting of the American Society of Human Genetics (ASHG) in Montréal on October 20, 2026, titled “Managing an R&D portfolio through the critical value creation period (CVCP).” The slides and a companion guide with full references are available here and here. A PDF copy of this blog, which was created with the help of AI, can be found here.
A paradox at the heart of drug R&D
Biopharmaceutical R&D does not lack investment. Investor funding for AI-enabled drug discovery roughly doubled between 2023 and 2025, to about $8.4 billion, and the headline value of AI partnerships signed by the 20 largest pharmaceutical companies reached about $30 billion in 2025 (McKinsey & Company, 2026). The number of active pipeline programs nearly doubled between 2015 and 2024. Yet R&D remains inefficient and expensive, and productivity has not kept pace with spending. The question is not how much capital the industry invests, but how effectively that capital is deployed to improve R&D productivity.
Too often, that capital returns to the same biology. Over the same decade, the number of novel targets entering the pipeline each year fell by about 70% (L.E.K.
… Read full article...
Part 1: What human genetics of lipids and psychiatric disease teach us about the answer to: What fraction of causal human biology do we actually know?
I have been asking a version of this question for months now – at lunch, over dinner, on walks with friends and colleagues. For fun, I have gotten into the habit of politely pressuring people for a single number. Come on, just give me your best guess!
Almost without exception, as we worked through the answers together, we did not land on a single number. Instead, we debated a structure. This blog is an attempt to write that structure down.
Every one of those conversations eventually ran into the same unexamined assumption behind most discussions of AI and drug discovery: that we already understand causal human biology well enough for a sufficiently capable model to find the cures hiding inside it.
If that assumption is right, as many with computational or technology backgrounds seem to believe, the job left for AI is largely one of connection and interpretation. Compress the literature, integrate the omics, surface the pattern a human missed.
If it is wrong, as many with biological or medical backgrounds seem to believe, if what we are actually missing is not connective tissue between known facts but the causal facts themselves, then AI’s job is a different and much harder one.…
Read full article...
