{
  "abstract": "Structure based generative model explore chemical spaces inside the protein active site , typically prioritizing candidates through empirical scoring functions.Few evaluate wthether the results is selective for the intended protein over a close paralogue , and none of them combines that evaluation with residue level conditioning and covalent bounds placement . In this paper we describe a design loop in which each rounds installs one chemical group aimed at one named pocket residue that a pairwise alignment marks as divergent in a chosen anti target , subject while enforcing structural continuity by requiring each successive molecule to retain its predecessor as a substructure. The anti target pocket is built by mutating only the divergent position within the target's own structure , so the two differ at those residues and nowhere else . Across 17 design campaigns the selected scaffold was retained in every final lead . Comparison against 44 reference compounds from the literature that target 12 receptor pair demonstrated preservation of established recognition chemistry in 19 comparisons , against a 12.6% unrelated active decoy rate . generated candidates the structure relationships with established inhibitor , including 78% shared core with imatinib and a 59% with erlotinib while adding alternative chemical strategies . These included residue directed electrophile warhead such as a chloroacetamide targeting ABL1 Cys388, and the generation of a macrocyclic peptidomimetic architecture for KRAS G12C. Together, these results demonstrate an integrated computational framework capable of translating therapeutic objectives and structural evidence into traceable medicinal chemistry design decisions across multiple protein targets.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Ahmed amine Sadouk",
      "orcid": "0009-0009-3258-6601"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2j55f835gxka6.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009331/v1",
  "fulltext_url": null,
  "id": "2j55f835gxka6",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009331/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7213907910",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009331/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/2j55f835gxka6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15009331",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009331/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "Residue-Conditioned In-Pocket Design for Paralogue Selectivity",
  "title_text": "Residue-Conditioned In-Pocket Design for Paralogue Selectivity",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/2j55f835gxka6",
  "venue": "ChemRxiv",
  "versions": []
}