With increasing cost and failure rates in the pharmaceutical R&D process not fundamentally improving over the last decade, pressure remains high to increase the probability of success to improve the effectiveness of pharmaceutical R&D. The broad introduction of AI into the R&D landscape over the last years holds the promise to lift pharmaceutical R&D out of its productivity problem, as preliminary analyses suggest that "AI-native" companies may be outpacing traditional peers. However, harnessing this potential requires moving beyond measuring technical model performance (e.g., predictive accuracy) to measuring strategic impact. In this perspective, members of the EFMC2 community-focused on advancing the collaboration between computational and medicinal chemists-discuss the challenges of applying key performance indicators (KPIs) in the idiosyncratic environment of drug discovery. We argue that the shift from expert-driven computer-aided drug design (CADD) to semiautonomous AI necessitates a new framework of impact-oriented KPIs. We provide recommendations for designing these strategic indicators to drive adoption, foster innovation, and objectively assess whether digital tools are delivering top-line impact.
Theis et al. (Sun,) studied this question.