Bruno Filoche-Rommé

Drug discovery requires traversing vast chemical spaces to identify compounds exhibiting favorable potency, selectivity and absorption–distribution–metabolism–excretion–toxicity (ADMET) profiles. During this process, the synthetic and assay throughput is generally markedly lower than the ideation of new propositions by the project team members, so that the prioritization of new syntheses is indispensable. We introduce a framework for simulating the outcome of multi-objective prioritization strategies during lead optimization. Based on the Design–Make–Test–Analyze (DMTA) paradigm, historical discovery programs are replayed round by round using user-defined compound selection strategies. We developed qualitative and quantitative tools to assess their performance in retrieving the best compounds and exploring the project’s chemical space. We demonstrate our pipeline using four industrial datasets, each containing chemical structures, assays values and time stamps. Multiple selection strategies are implemented, including approaches inspired by active learning (AL), multicriteria decision analysis (MCDA), and medicinal chemistry heuristics, that display distinct behavior in the selection of compounds. Retrospective analysis provides a rigorous, low-cost test bed for investigating selection strategies in lead optimization and could help reducing cost, duration and risk of lead optimization projects.

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.