Learning from the successes and failures of early artificial intelligence (AI) adoption for drug discovery in Big BioPharma

INTRODUCTION: AI has tremendous potential to reduce time and costs taken to discover and develop new medical entities. As technology evolves, it is essential to learn from successes and failures to realign expectations for scientists, stakeholders and investors. AREAS COVERED: The authors discuss the challenges associated with the traditional reductionist approach to drug discovery which relies on incomplete data for target validation and, specifically for small molecules, the expanse of chemical space providing potential candidates. The promise of AI is illustrated by both early success and failure stories. Lessons learned are provided at levels of realism, adoption and integration of AI within current Research and Development (R&D) organizational structures. EXPERT OPINION: The first decade of AI adoption in Big BioPharma has been characterized by genuine breakthroughs and sobering realities. While AI has delivered notable accelerations in hit identification and early-stage design, it has yet to fundamentally alter the success rates of late-stage clinical trials. The industry has learned that AI is neither a silver bullet nor a passing fad, though a critical and evolving component of modern R&D. By consolidating lessons from early adoption, the next decade may see AI truly shift the innovation frontier in global pharmaceutical discovery.