Over the past decade, collective intelligence, i.e., the intelligence that emerges from collective efforts, has transformed complex problem-solving and decision-making. In drug discovery, decision-making often relies on medicinal chemistry intuition. The present study explores the application of collective intelligence in drug discovery, focusing on lead optimization. Ninety-two Sanofi researchers with diverse expertise participated anonymously in an exercise centered on ADMET-related questions. Their feedback was used to build a collective intelligence agent, which was compared to an artificial intelligence model. The study led to three major conclusions: first, collective intelligence improves decision-making in optimizing ADMET endpoints, compared to individual decisions. Second, collective intelligence outperforms artificial intelligence for all other endpoints but hERG inhibition. Finally, we observe complementarity between collective human and artificial intelligence. Overall, this research highlights the potential of collective intelligence in drug discovery and the importance of a synergistic approach combining human and artificial intelligence in project decision making.

In chemoinformatics, artificial intelligence (AI) continues to grow a symbiosis with open science (OS). Such a close AI-OS interaction brings substant…

We present a simple method for assigning accurate confidence levels to molecular property predictions from regression models. These confidence levels are easy to interpret and useful for making decisi...