量子计算增强算法揭示潜在的KRAS抑制剂。
Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors.
作者信息
Ghazi Vakili Mohammad, Gorgulla Christoph, Snider Jamie, Nigam AkshatKumar, Bezrukov Dmitry, Varoli Daniel, Aliper Alex, Polykovsky Daniil, Padmanabha Das Krishna M, Cox Iii Huel, Lyakisheva Anna, Hosseini Mansob Ardalan, Yao Zhong, Bitar Lela, Tahoulas Danielle, Čerina Dora, Radchenko Eugene, Ding Xiao, Liu Jinxin, Meng Fanye, Ren Feng, Cao Yudong, Stagljar Igor, Aspuru-Guzik Alán, Zhavoronkov Alex
机构信息
Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
Department of Chemistry, University of Toronto, Toronto, Ontario, Canada.
出版信息
Nat Biotechnol. 2025 Jan 22. doi: 10.1038/s41587-024-02526-3.
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.
我们引入了一种用于小分子设计的量子-经典生成模型，特别针对用于癌症治疗的KRAS抑制剂。我们应用该方法设计、筛选并合成了15种拟议中的分子，这些分子有望与KRAS显著结合用于癌症治疗，其中有两种作为抑制剂具有未来开发的潜力。这项工作展示了量子计算在生成经实验验证的、与经典模型相比具有优势的命中分子方面的潜力。
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