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AI-driven discovery of synergistic drug combinations against pancreatic cancer

Author

Listed:
  • Mohsen Pourmousa

    (9800 Medical Center Drive)

  • Sankalp Jain

    (9800 Medical Center Drive)

  • Elena Barnaeva

    (9800 Medical Center Drive)

  • Wengong Jin

    (Massachusetts Institute of Technology)

  • Joshua Hochuli

    (University of North Carolina)

  • Zina Itkin

    (9800 Medical Center Drive)

  • Travis Maxfield

    (University of North Carolina)

  • Cleber Melo-Filho

    (University of North Carolina)

  • Andrew Thieme

    (University of North Carolina)

  • Kelli Wilson

    (9800 Medical Center Drive)

  • Carleen Klumpp-Thomas

    (9800 Medical Center Drive)

  • Sam Michael

    (9800 Medical Center Drive)

  • Noel Southall

    (9800 Medical Center Drive)

  • Tommi Jaakkola

    (Massachusetts Institute of Technology)

  • Eugene N. Muratov

    (University of North Carolina
    LLC)

  • Regina Barzilay

    (Massachusetts Institute of Technology)

  • Alexander Tropsha

    (University of North Carolina
    LLC)

  • Marc Ferrer

    (9800 Medical Center Drive)

  • Alexey V. Zakharov

    (9800 Medical Center Drive)

Abstract

Pancreatic cancer treatment often relies on multi-drug regimens, but optimal combinations remain elusive. This study evaluates predictive approaches to identify synergistic drug combinations using a dataset from the National Center for Advancing Translational Sciences (NCATS). Screening 496 combinations of 32 anticancer compounds against the PANC-1 cells experimentally determined the degree of synergism and antagonism. Three research groups (NCATS, University of North Carolina, and Massachusetts Institute of Technology) leverage these data to apply machine learning (ML) approaches, predicting synergy across 1.6 million combinations. Of the 88 tested, 51 show synergy, with graph convolutional networks achieving the best hit rate and random forest the highest precision. Beyond highlighting the potential of ML, this work delivers 307 experimentally validated synergistic combinations, demonstrating its practical impact in treating pancreatic cancer.

Suggested Citation

  • Mohsen Pourmousa & Sankalp Jain & Elena Barnaeva & Wengong Jin & Joshua Hochuli & Zina Itkin & Travis Maxfield & Cleber Melo-Filho & Andrew Thieme & Kelli Wilson & Carleen Klumpp-Thomas & Sam Michael , 2025. "AI-driven discovery of synergistic drug combinations against pancreatic cancer," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56818-6
    DOI: 10.1038/s41467-025-56818-6
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    References listed on IDEAS

    as
    1. Feixiong Cheng & István A. Kovács & Albert-László Barabási, 2019. "Network-based prediction of drug combinations," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    2. Feixiong Cheng & István A. Kovács & Albert-László Barabási, 2019. "Publisher Correction: Network-based prediction of drug combinations," Nature Communications, Nature, vol. 10(1), pages 1-1, December.
    3. Yi Sun & Zhen Sheng & Chao Ma & Kailin Tang & Ruixin Zhu & Zhuanbin Wu & Ruling Shen & Jun Feng & Dingfeng Wu & Danyi Huang & Dandan Huang & Jian Fei & Qi Liu & Zhiwei Cao, 2015. "Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer," Nature Communications, Nature, vol. 6(1), pages 1-10, December.
    Full references (including those not matched with items on IDEAS)

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