Harnessing Medicinal Chemical Intuition from Collective Intelligence

Abstract

Over the last decade, the combination of collective intelligence with computational methods has transformed complex problem-solving. Here, we investigate if and how collective intelligence can be applied to drug discovery, focusing on the lead optimization stage of the discovery process. For this study, 92 Sanofi researchers with diverse scientific expertise participated anonymously in a lead optimization exercise. Their feedback was used to build a collective intelligence agent that was compared to an artificial intelligence model developed in parallel. This work has led to three major conclusions. First, a significant improvement of collective versus individual decisions in optimizing ADMET endpoints is observed. Second, for all endpoints apart from hERG inhibition, the collective intelligence performance exceeds the artificial intelligence model. Third, we observe a complementarity between collective intelligence and AI for complex tasks, demonstrating the potential of hybrid predictions. Overall, this research highlights the potential of collective intelligence in drug discovery. The entire dataset, including questionnaire responses, and developed models are available for access on GitHub.