@inproceedings{dey-etal-2025-large,
    title = "Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization",
    author = "Dey, Vishal  and
      Hu, Xiao  and
      Ning, Xia",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1145/",
    doi = "10.18653/v1/2025.findings-emnlp.1145",
    pages = "20996--21023",
    ISBN = "979-8-89176-335-7",
    abstract = "In real-world drug design, molecule optimization requires selectively improving multiple molecular properties up to pharmaceutically relevant levels, while maintaining others that already meet such criteria. However, existing computational approaches and instruction-tuned LLMs fail to capture such nuanced property-specific objectives, limiting their practical applicability. To address this, we introduce C-MuMOInstruct, the first instruction-tuning dataset focused on multi-property optimization with explicit, property-specific objectives. Leveraging C-MuMOInstruct, we develop $\mathtt{GeLLM^4O\text{-}C}$s, a series of instruction-tuned LLMs that can perform targeted property-specific optimization. Our experiments across 5 in-distribution and 5 out-of-distribution tasks show that $\mathtt{GeLLM^4O\text{-}C}$s consistently outperform strong baselines, achieving up to 126{\%} higher success rate. Notably, $\mathtt{GeLLM^4O\text{-}C}$s exhibit impressive 0-shot generalization to novel optimization tasks and unseen instructions. This offers a step toward a foundational LLM to support realistic, diverse optimizations with property-specific objectives. C-MuMOInstruct and code are accessible through https://github.com/ninglab/GeLLMO-C."
}