@inproceedings{wang-etal-2026-molmem,
    title = "{M}ol{M}em: Memory-Augmented Agentic Reinforcement Learning for Sample-Efficient Molecular Optimization",
    author = "Wang, Ziqing  and
      Wen, Yibo  and
      Pandey, Abhishek  and
      Liu, Han  and
      Ding, Kaize",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.2024/",
    pages = "43694--43712",
    ISBN = "979-8-89176-390-6",
    abstract = "In drug discovery, molecular optimization aims to iteratively refine a lead compound to improve molecular properties while preserving structural similarity to the original molecule. However, each oracle evaluation is expensive, making sample efficiency a key challenge for existing methods under a limited oracle budget. Trial-and-error approaches require many oracle calls, while methods that leverage external knowledge tend to reuse familiar templates and struggle on challenging objectives. A key missing piece is long-term memory that can ground decisions and provide reusable insights for future optimizations. To address this, we present MolMem (\textbf{Mol}ecular optimization with \textbf{Mem}ory), a multi-turn agentic reinforcement learning (RL) framework with a dual-memory system. Specifically, MolMem uses Static Exemplar Memory to retrieve relevant exemplars for cold-start grounding, and Evolving Skill Memory to distill successful trajectories into reusable strategies. Built on this memory-augmented formulation, we train the policy with dense step-wise rewards, turning costly rollouts into long-term knowledge that improves future optimization. Extensive experiments show that MolMem achieves 90{\%} success on single-property tasks (1.5$\times$ over the best baseline) and 52{\%} on multi-property tasks using only 500 oracle calls. Our code is available at \url{https://github.com/REAL-Lab-NU/MolMem}."
}