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PreprintCC BYChemRxiv

AI-driven design of a novel synthetic route to malaria drug ganaplacide

  • Fernando F. Huerta1
  • Erik Leonhardt2
  • Lianyan L. Xu2
  • Trevor Laird3
  • John Dillon4
  • David M. Parry1
  • Thomas Galeandro-Diamant1
4 affiliations
  1. 1ChemAI Ltd.
  2. 2Exemplify Biopharma, Inc.
  3. 3Trevor Laird Associates Ltd.
  4. 4JLD Pharma Consulting LLC
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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.

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Cite

BibTeXRIS
BibTeX
@misc{huerta2026aidriven,
  title = {{AI-driven design of a novel synthetic route to malaria drug ganaplacide}},
  author = {Fernando F. Huerta and Erik Leonhardt and Lianyan L. Xu and Trevor Laird and John Dillon and David M. Parry and Thomas Galeandro-Diamant},
  howpublished = {ChemRxiv},
  year = {2026},
  month = oct,
  doi = {10.26434/chemrxiv.15006146/v3},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15006146/v3},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - AI-driven design of a novel synthetic route to malaria drug ganaplacide
AU  - Fernando F. Huerta
AU  - Erik Leonhardt
AU  - Lianyan L. Xu
AU  - Trevor Laird
AU  - John Dillon
AU  - David M. Parry
AU  - Thomas Galeandro-Diamant
PY  - 2026
DA  - 2026/10/22
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15006146/v3
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15006146/v3
AB  - 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.
ER  -