Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors

作者：Mohammad Ghazi Vakili, Christoph Gorgulla, Jamie Snider, AkshatKumar Nigam, Dmitry Bezrukov, Daniel Varoli, Alex Aliper, Daniil Polykovsky, Krishna M. Padmanabha Das, Huel Cox Iii, A. Lyakisheva, Ardalan Hosseini Mansob, Zhong Yao, L. Bitar, Danielle Tahoulas, Dora Čerina, E. Radchenko, Xiao Ding, Jinxin Liu, Fanye Meng, F. Ren, Yudong Cao, Igor Štagljar, Alán Aspuru-Guzik, A. Zhavoronkov · 发表于：Nature Biotechnology · 年份：2025 · DOI：10.1038/s41587-024-02526-3 · 被引用次数：73 · 研究领域：Medicine

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. A hybrid model combines quantum and classical approaches to generate compounds targeting the KRAS protein.