Artificial intelligence in drug discovery - what it is, where we stand and the path forward.

AI in drug discovery has shown limited clinically relevant impact despite decades of development, highlighting the need for improved translational focus.

• Why it matters: Bridging the gap between AI advancements and real-world clinical outcomes is crucial to deliver safer, more effective medicines faster to patients, addressing a significant unmet need.
• What they did: The authors reviewed progress in AI methods for drug discovery, identifying challenges such as insufficient clinical translation, data application difficulties, and problem underspecification, and proposed strategic recommendations.
• The result: Focusing on decision-making improvements and better benchmarking can enhance AI's translational relevance, enabling more effective integration into drug development pipelines and ultimately benefiting patient care.