{
  "abstract": "Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple properties while maintaining a similar overall structure. This process often requires iterative modifications guided by computational or experimental feedback. Recent large language model (LLM)-based methods have begun to explore this iterative approach; however, these methods are not explicitly trained to leverage such feedback, while existing approaches primarily focus on single-turn molecular optimization. To address this gap, we introduce Multi-turn Feedback Guided (MFG) molecular optimization, a reinforcement learning framework that trains an LLM to progressively optimize molecules using feedback from previous turns. MFG introduces a feedback vector capturing both property improvements and source molecule similarity, alongside a feedback-aligned reward function that promotes progressive improvement across turns. We apply MFG over $\\text{GeLLM}^{3}\\text{O}$, a state-of-the-art LLM model for multi-objective molecular optimization, evaluate $\\text{GeLLM}^{3}\\text{O-MFG}$ on ten multi-objective molecular optimization tasks. The results demonstrate that $\\text{GeLLM}^{3}\\text{O-MFG}$ achieves the highest average similarity-constrained success rate ($81.50%$), outperforming the base model $\\text{GeLLM}^{3}\\text{O}$ ($70.81%$).",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.18851",
  "author_total": 5,
  "authors": [
    {
      "name": "Trieu Nguyen"
    },
    {
      "name": "Frazier N. Baker"
    },
    {
      "name": "Ruoxi Gao"
    },
    {
      "name": "Daniel Adu-Ampratwum"
    },
    {
      "name": "Xia Ning"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fckv9fe1ge5aj.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.18851",
  "fulltext_url": null,
  "id": "fckv9fe1ge5aj",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.18851",
  "language": null,
  "licence": "CC-BY-SA-4.0",
  "licence_label": "CC BY-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7221770030",
  "pdf_url": "https://arxiv.org/pdf/2610.18851",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fckv9fe1ge5aj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:21Z",
      "native_id": "2610.18851",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.18851"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "MFG: Multi-turn Feedback Guided Molecular Optimization",
  "title_text": "MFG: Multi-turn Feedback Guided Molecular Optimization",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:21Z",
  "url": "https://research.draconitic.com/paper/fckv9fe1ge5aj",
  "venue": "arXiv",
  "versions": []
}