Sales of AI-discovered Drugs

Several high-forecast respondents believe that AI will accelerate discovery-to-market timelines through faster design-make-test-analyze loops and potentially AI-enabled pharmacodynamic simulations that could streamline clinical trials. Others note that drug discovery-to-market timelines can be shortened significantly during times of crisis via EUAs (emergency use authorizations). Low-forecast respondents commonly emphasize regulatory realities that may limit AI's impact on approval timelines: “Given that the median time it takes to get through the FDA approval process is over 10 years, and no AI-discovered drugs appear to have started Phase III trials yet, 2027 is likely too soon for many, if any, new AI drugs to be approved."L

Many high-forecast respondents note that AI-discovered drugs already demonstrate significantly higher Phase I success rates, and that “extrapolating from current rates of increase in the number of proposed AI drugs, these will constitute a majority of new clinical trial submissions.” Several low-percentage forecasters, however, think that “the turnaround time between Phase I and approval will not speed up substantially for AI-invented drugs,” because “early entrants sped through Phase I but then quickly reverted to the mean in Phase II.”

Many high-forecast respondents expect that by 2040, AI will become “a standard discovery tool.” One writes, “by then, I expect AI to be fully embedded in how drug discovery is done” and another that, “as some companies invest in AI adoption and see payoffs, more competitors and startups will do the same in order to compete.” But several low-percentage forecasters argue institutional inertia will slow adoption: “While there are some pharma companies that have widely embraced the usage of deep learning models...much of the industry is fairly slow moving to adopt new techniques....[The] market shift will likely take a decade at least to fully change the nature of biomedical research.”

Most high-forecast respondents interpret the term broadly, with one expert noting, “It gets tricky to pin down what counts as an AI-discovered drug. Based on you using reports like the Boston Consulting Group… I'm assuming you go for a fairly broad sense, i.e., that earlier attempts at ML assistance in drug discovery count as AI.” Low-percentage forecasters tend to question whether narrow tools will count: “Traditional methods (including Bayesian methods and random forest) with simple computational features can already perform well on drug property prediction...do Bayesian methods and random forest count as AI?”

Some high-forecast respondents hypothesize that AI-discovered blockbusters may dominate sales: “Sales will probably follow a power law of some sort (i.e., a small number of drugs will have a large number of sales). If AI invents one or more blockbusters, then sales might be highly skewed.” Echoing that sentiment, another writes, “there's a fat tail from the possibility that one or more AI-discovered wonder drugs gets rapid approval and huge sales.” Several low-forecast respondents point to the possibility that AI may instead “increase the availability of treatments for rare diseases, but these drugs would likely not make up a large part of total new drug sales.”

A few high-percentage forecasters cite the potential for deregulation under the current administration and emphasize what seems to be a “bipartisan recommendation to reduce [the] FDA drug timeline.” Several low-percentage forecasters see the current political climate as constraining: “I kept my estimates low in the near term because of the ongoing disruption to FDA approval and drug discovery pipelines under the current U.S. Government and Trump Administration.”