Agentic Systems for Molecular Sciences

Agentic large language model systems are rapidly moving from conversational assistants to "co-scientists" in the molecular sciences, planning syntheses, calling domain-specific tools, and closing experimental loops with laboratory automation. Recent demonstrations span autonomous drug repurposing, retrosynthesis planning, and genome-wide virtual screening, all built on orchestrated stacks of learned representations, predictors, and simulators. Yet careful benchmarking has exposed striking failures at seemingly simple tasks: tool-augmented agents reach only around 50% accuracy on chemical cost estimation, chemistry-oriented language models fail systematic symbolic reasoning on molecular graphs, and single-cell foundation models for perturbation prediction do not outperform linear baselines. This workshop takes the contrast between agentic ambition and methodological fragility as its starting point, with explicit space for negative results, rigorous baselines, and benchmark contributions alongside methodological advances. We invite researchers across machine learning for chemistry, structural biology, drug discovery, materials, and the methodological core of geometric and generative deep learning to join us in Paris.