{
  "annotation_id": "b8747023-8fed-48e9-93fb-4e8f094f094c",
  "date_created": "2026-03-02T18:01:34.798000Z",
  "date_modified": "2026-03-02T18:01:34.798000Z",
  "file_hash": "796688c336472a394a8a4705f240d9cc218648ab2e1d6a7da325c3e8e7e2c718",
  "private": false,
  "record": {
    "abstract": "Despite improved rational drug design and a remarkable progress in genomic,\nproteomic and high-throughput screening methods, the number of novel,\nsingle-target drugs fell much behind expectations during the past decade.\nMulti-target drugs multiply the number of pharmacologically relevant target\nmolecules by introducing a set of indirect, network-dependent effects. Parallel\nwith this the low-affinity binding of multi-target drugs eases the constraints\nof druggability, and significantly increases the size of the druggable\nproteome. These effects tremendously expand the number of potential drug\ntargets, and will introduce novel classes of multi-target drugs with smaller\nside effects and toxicity. Here we review the recent progress in this field,\ncompare possible network attack strategies, and propose several methods to find\ntarget-sets for multi-target drugs.",
    "arxiv_id": "q-bio/0703010",
    "authors": [
      "Tamas Korcsmaros",
      "Mate S. Szalay",
      "Csaba Bode",
      "Istvan A. Kovacs",
      "Peter Csermely"
    ],
    "categories": [
      "q-bio.MN",
      "q-bio.BM"
    ],
    "journal_ref": "Expert Opinion on Drug Discovery (2007) 2:1-10",
    "title": "How to design multi-target drugs: Target search options in cellular   networks",
    "url": "https://arxiv.org/abs/q-bio/0703010"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "e79a1f54-4fb9-4792-8a2b-6fed1cc2633b",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}