In a recent study published in Nature Biotechnology,Mohammad Ghazi Vakili et al.applied quantum computing and generative machine learning-specifically Quantum Circuit Born Machines(QCBMs)and Long Short-Term Memory(LST...In a recent study published in Nature Biotechnology,Mohammad Ghazi Vakili et al.applied quantum computing and generative machine learning-specifically Quantum Circuit Born Machines(QCBMs)and Long Short-Term Memory(LSTM)networks—to efficiently explore high-dimensional chemical space and identify structurally novel KRAs inhibitors.This research highlights how quantum-enhanced Al(artificial intelligence),when supported by substantial pre-existing data,can contribute to the discovery of inhibitors for challenging targets such as KRAs.展开更多
基金supported by the KRIBB Research Initiative Program(KGM4252533,KGM5162524)supported by a Korea Basic Science Institute grant(H.J.C.,AC202402).
文摘In a recent study published in Nature Biotechnology,Mohammad Ghazi Vakili et al.applied quantum computing and generative machine learning-specifically Quantum Circuit Born Machines(QCBMs)and Long Short-Term Memory(LSTM)networks—to efficiently explore high-dimensional chemical space and identify structurally novel KRAs inhibitors.This research highlights how quantum-enhanced Al(artificial intelligence),when supported by substantial pre-existing data,can contribute to the discovery of inhibitors for challenging targets such as KRAs.
