William Blair: From Code to Clinic How AI Is (and Isn’t) Rewriting the Life of a Drug

Portfolio Manager Summary

Here is the short version: AI is not replacing the physical work of drug discovery, but it is reshaping it. It is true that AI is increasingly being leveraged as a “dry lab” (i.e., computational) tool for hypothesis generation, but those hypotheses will always need to be physically tested in the “wet lab” (i.e., the real world) and the overwhelming majority of drug discovery spend relates to the work of proving what’s true in the real world. Hence, the “wet lab obsolescence” narrative is overstated and misframed.

Separately, the most balanced assessment of AI in drug discovery today is that it is good at generating ideas. Not necessarily good ideas, but lots of them—quickly. Like all AI, the quality of outputs relies on the quality of inputs, and biology today lacks the requisite inputs (standardized, fit-for purpose, experimental data) to generate desired outputs (novel drug ideas). Hence, for AI to reach its potential in biology, a significant amount of new experimental data needs to be generated, creating a durable tailwind for the tool stack sitting at those data bottlenecks.

On the stocks: the life science tools and services group has traded off categorically in recent weeks, partly on fears that AI will dramatically reduce wet lab utilization in drug discovery. However, on balance we view AI as a net tailwind for most companies we cover given where their products sit in the drug discovery workflow. Thus, the setup is asymmetric—alpha is available both if AI becomes a ubiquitous tool in drug discovery (underappreciated flow-through to physical lab work) and if it does not (dispelling the wet lab obsolescence thesis). Investors should be adding exposure as endmarkets continue to heal and growth accelerates throughout 2026.