{
  "abstract": "Malaria is a deadly infectious disease that disproportionately affects low- and middle-income countries. Ganaplacide is a drug candidate that is highly effective against malaria, and the ability to produce it at low cost is essential. Here we show how we used a combination of AI-driven design and expert chemistry knowledge to develop a novel synthetic route to ganaplacide. The AI retrosynthesis algorithm suggested a non-evident two-steps synthesis of the imidazolopiperazine core of the molecule from benzyl-protected dimethylpiperazinone through a strategic thioimidate intermediate. We also present the Synthetic Confidence Score, which assesses the level of risk associated with a given retrosynthetic disconnection and allows balancing creativity and reliability. The synergy between AI creativity, metrics such as the Synthetic Confidence Score and expert chemist knowledge has the potential to unlock non-evident efficient routes for important molecules, when expert chemists alone have explored mainstream chemistry unsuccessfully.",
  "abstract_shown": true,
  "affiliations": [
    "ChemAI Ltd.",
    "Exemplify Biopharma, Inc.",
    "Trevor Laird Associates Ltd.",
    "JLD Pharma Consulting LLC"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Fernando F. Huerta",
      "orcid": "0000-0002-6686-8895"
    },
    {
      "affs": [
        1
      ],
      "name": "Erik Leonhardt",
      "orcid": "0000-0002-5380-2328"
    },
    {
      "affs": [
        1
      ],
      "name": "Lianyan L. Xu"
    },
    {
      "affs": [
        2
      ],
      "name": "Trevor Laird"
    },
    {
      "affs": [
        3
      ],
      "name": "John Dillon"
    },
    {
      "affs": [
        0
      ],
      "name": "David M. Parry"
    },
    {
      "affs": [
        0
      ],
      "name": "Thomas Galeandro-Diamant",
      "orcid": "0000-0002-1068-013X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/egss5ankye3e4.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15006146/v3",
  "fulltext_url": null,
  "id": "egss5ankye3e4",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15006146/v3",
  "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": "W7223369941",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15006146/v3",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/egss5ankye3e4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:26Z",
      "native_id": "10.26434/chemrxiv.15006146",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15006146/v3"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "AI-driven design of a novel synthetic route to malaria drug ganaplacide",
  "title_text": "AI-driven design of a novel synthetic route to malaria drug ganaplacide",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:26Z",
  "url": "https://research.draconitic.com/paper/egss5ankye3e4",
  "venue": "ChemRxiv",
  "versions": []
}