MolRSI

The team introduces MolRSI, a recursive self-improving agent for molecular discovery that folds model choice, screening-rule composition and candidate prioritization into its search space, then iteratively refines its workflow using experience from prior tasks. In an antibacterial screen over roughly 12.1 million compounds, it reached a 15.9% wet-lab hit rate versus 8.7% for a human-expert-designed non-agentic workflow, a relative gain of about 82%; on de novo pMHC-binding protein design it produced 8 hits meeting specificity criteria versus 0 for a Claude Science baseline. The work extends the recursive self-improvement loop into a domain gated by wet-lab validation.