Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors
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. A hybrid model combines quantum and classical approaches to generate compounds targeting the KRAS protein.
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