{
  "abstract": "Over the past decade, artificial intelligence (AI) has increasingly influenced the early stages of drug discovery, which focus primarily on target evaluation and the design or identification of new active small molecules or biologics. Deep neural networks adapted from areas such as natural language processing and computer vision have provided novel opportunities for generative modeling and human–machine interactions. State‐of‐the‐art AI models for generative molecular design have emerged, but remain only little understood so far. Furthermore, new computational systems are being devised rapidly that are expected to significantly transform the drug discovery landscape in the near future. These developments pose challenges to computer‐aided drug discovery in various ways. In the following, we discuss these opportunities and challenges, with a focus on molecular design.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Life Science Informatics and Data Science B‐IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry Rheinische Friedrich‐Wilhelms Universität Bonn Germany",
    "Lamarr Institute for Machine Learning and Artificial Intelligence Rheinische Friedrich‐Wilhelms‐Universität Bonn Bonn Germany",
    "Data Science Center Nara Institute of Science and Technology Ikoma Nara Japan"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Andrea Mastropietro",
      "orcid": "0000-0002-3456-9428"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Jürgen Bajorath",
      "orcid": "0000-0002-0557-5714"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/aqtre9c8xzwhe.bib",
  "cited_by": 0,
  "doi": "10.1002/cmdc.70564",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13735986/",
  "id": "aqtre9c8xzwhe",
  "issns": [
    "1860-7179",
    "1860-7187"
  ],
  "landing_url": "https://doi.org/10.1002/cmdc.70564",
  "language": "en",
  "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": [
    [
      "heading",
      "Perspective"
    ],
    [
      "openalex",
      "3002|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7225335475",
  "pdf_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cmdc.70564",
  "pmcid": "PMC13735986",
  "pmid": "42984375",
  "published": "2026-11-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/aqtre9c8xzwhe.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:21:33Z",
      "native_id": "PMC13735986",
      "source": "pmc",
      "url": "https://doi.org/10.1002/cmdc.70564"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:3002|Computational Drug Discovery Methods",
      "subfield": "medicinal"
    }
  ],
  "title": "From Generative Molecular Design to Agentic Artificial Intelligence in Drug Discovery",
  "title_text": "From Generative Molecular Design to Agentic Artificial Intelligence in Drug Discovery",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:21:33Z",
  "url": "https://research.draconitic.com/paper/aqtre9c8xzwhe",
  "venue": "Chemmedchem",
  "versions": []
}