Majority of scientists now believe AI is accelerating drug discovery, survey shows
A new survey by Endpoints Signal reveals that 78% of biopharma scientists now expect artificial intelligence to significantly speed up drug discovery in 2026. This marks a sharp increase from last year, when skepticism was more common. The shift reflects growing real-world success and integration of AI tools across research pipelines.
A recent survey conducted by Endpoints Signal indicates that 78% of biopharma scientists now anticipate artificial intelligence will significantly accelerate drug discovery by 2026, a notable rise from previous years when skepticism was more prevalent. This shift reflects increasing confidence in AI’s practical utility within the pharmaceutical research pipeline. The data underscores a growing consensus that AI tools are no longer experimental but are being integrated into real-world drug development processes. While the survey does not specify individual companies or locations, it highlights a broad trend across the biopharma sector toward adopting AI-driven methods for tasks such as target identification, compound screening, and clinical trial design.
The change in sentiment follows earlier reports of limited success and high expectations that were not always met, leading to cautious optimism. However, recent demonstrable results—such as faster identification of promising drug candidates or reduced time-to-trial—have bolstered credibility. Scientists involved in the field are now more willing to invest time and resources into AI platforms, signaling a maturation of the technology’s role in medicine. The survey’s findings suggest that organizations developing or deploying AI tools in biopharma may see increased adoption and funding as confidence grows.
What remains unclear is how this shift will affect traditional research roles, regulatory pathways, or the pace of clinica
The growing belief in AI’s role in drug discovery could reshape pharmaceutical innovation, potentially reducing development timelines and costs—key factors in making new treatments more accessible. Patients with rare or urgent conditions may benefit fastest from accelerated pipelines, though equitable access remains a concern. The market for AI in biopharma may expand rapidly, influencing venture capital flows and corporate R&D strategies. Comparable to the rise of genomics in the early 2000s, AI could become foundational to drug development, altering how companies compete. However, this shift may also challenge traditional research roles and raise questions about data ownership, algorithmic bias, and regulatory oversight. Institutions like the FDA may need to adapt approval processes for AI-generated insights. What’s next could include broader validation of AI-driven drugs in late-stage trials or increased public scrutiny over transparency. The pace of change may depend on whether re