MoLRSI: A Recursive Self-Improving AI agent for Molecular Discovery

Key points

MolRSI converts scientific papers and code into validated executable skills, and uses experience-guided Monte Carlo tree search (MCTS) to assemble and refine workflows.
It stores workflow performance, refinement outcomes, and execution records from 25 source tasks in persistent memory to recursively improve the search policy for later tasks.
In virtual antibiotic screening, 17 of 107 compounds showed antibacterial activity with MICs of 32µg/mL or lower, and the experimental hit rate of 15.9% exceeded the 8.7% of the previous expert-driven workflow.
In de novo peptide-MHC binder design, it produced 8 experimental hits in total, 5 for MART-1 and 3 for SARS-CoV-1, whereas Claude Science produced no hits under the same conditions.
The small-molecule resource library consists of 110 reusable skills (55 models, 48 methods, 7 tools), and the protein library consists of 65 skills.
As a limitation, the authors state that whether policy updates remain useful as biological targets and evaluation criteria change is an open question, and incorporating experimental outcomes into policy updates is future work.

External research summaries. These are not HDATF publications or measured product results.