{
  "abstract": "Motivation: Designing novel molecules that simultaneously satisfy several, often conflicting, properties is a long-standing challenge in drug design. While recent deep learning advances optimize single targets effectively, navigating the tradeoffs required for multiple objectives remains a difficult problem.\n\nResults: We introduce the Multi-Objective-Conditioned-Return-Blocks Decision Transformer (M-GoRT), a novel architecture for multi-target de-novo drug design incorporating return-to-go blocks conditioned on goal embeddings. Trained without exploration, reward weighting or online fine-tuning, M-GoRT efficiently optimizes for multiple targets. M-GoRT achieves competitive multi-objective success rates against state-of-the-art baselines while maintaining superior structural diversity and novelty, even in high-dimensional tasks incorporating biological targets (DRD2, JNK3). In these high-dimensional tasks, M-GoRT maintains superior structural diversity and novelty establishing it as a practical tool for multi-property molecular generation.\n\nAvailability and implementation: M-GoRT and the data used are freely available as an open-source software under the MIT license at GitHub https://github.com/eyalmazuz/MolGen/tree/GCRB-DT.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Computer and Information Sciences, Ben-Gurion University of the Negev , Be'er Sheva 8410501,"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Eyal Mazuz",
      "orcid": "0009-0001-1077-1736"
    },
    {
      "affs": [
        0
      ],
      "name": "Or Gindes"
    },
    {
      "affs": [
        0
      ],
      "name": "Lior Rokach"
    },
    {
      "affs": [
        0
      ],
      "name": "Bracha Shapira"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0g6275bbaemx4.bib",
  "cited_by": 0,
  "doi": "10.1093/bioinformatics/btag731",
  "fulltext_url": null,
  "id": "0g6275bbaemx4",
  "issns": [
    "1367-4803",
    "1367-4811"
  ],
  "landing_url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag731/8860138",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7218664982",
  "pdf_url": "https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btag731/71575342/btag731.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Oxford University Press (OUP)",
  "ris": "https://research.draconitic.com/paper/0g6275bbaemx4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:38Z",
      "native_id": "10.1093/bioinformatics/btag731",
      "source": "crossref",
      "url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag731/8860138"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "issn:1367-4803",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Multi-objective-conditioned-return-blocks decision transformer: a novel approach for multi-objective de novo drug design",
  "title_text": "Multi-objective-conditioned-return-blocks decision transformer: a novel approach for multi-objective de novo drug design",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:38Z",
  "url": "https://research.draconitic.com/paper/0g6275bbaemx4",
  "venue": "Bioinformatics",
  "versions": []
}