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. 2025 Jan 22.
doi: 10.1038/s41587-024-02526-3. Online ahead of print.

Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors

Mohammad Ghazi Vakili  1   2 , Christoph Gorgulla  3   4 , Jamie Snider  5 , AkshatKumar Nigam  6 , Dmitry Bezrukov  7 , Daniel Varoli  8 , Alex Aliper  7 , Daniil Polykovsky  9 , Krishna M Padmanabha Das  10   11 , Huel Cox Iii  11 , Anna Lyakisheva  5 , Ardalan Hosseini Mansob  5   12 , Zhong Yao  5 , Lela Bitar  5   13 , Danielle Tahoulas  5   14 , Dora Čerina  14   15 , Eugene Radchenko  7 , Xiao Ding  7 , Jinxin Liu  7 , Fanye Meng  7 , Feng Ren  7 , Yudong Cao  16 , Igor Stagljar  17   18   19   20 , Alán Aspuru-Guzik  21   22   23   24   25   26 , Alex Zhavoronkov  27
Affiliations

Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors

Mohammad Ghazi Vakili et al. Nat Biotechnol. .

Abstract

We introduce a quantum-classical generative model for small-molecule design, specifically targeting KRAS inhibitors for cancer therapy. We apply the method to design, select and synthesize 15 proposed molecules that could notably engage with KRAS for cancer therapy, with two holding promise for future development as inhibitors. This work showcases the potential of quantum computing to generate experimentally validated hits that compare favorably against classical models.

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Conflict of interest statement

Competing interests: A.A.G. serves as the chief visionary officer and is a board member of Kebotix. A.Z., A.A., D.B., D.P., X.D., J.L., E.R., F.R. and F.M. are affiliated with Insilico Medicine. Y.C. and D.V. are affiliated with Zapata AI. C.G. is a cofounder and consultant of Virtual Discovery and Quantum Therapeutics. The remaining authors declare no competing interests.

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