Quantum biological convergence: quantum computing accelerates KRAS inhibitor design
Abstract
In a recent study published in Nature Biotechnology , Mohammad Ghazi Vakili et al. applied quantum computing and generative machine learning — speci fi cally Quantum Circuit Born Machines (QCBMs) and Long Short-Term Memory (LSTM) networks — to ef fi ciently explore high-dimensional chemical space and identify structurally novel KRAS inhibitors. 1 This research highlights how quantum-enhanced AI (arti fi cial intelligence), when supported by substantial pre-existing data, can contribute to the discovery of inhibitors for challenging targets such as KRAS. 1 KRAS is a frequently mutated oncogene in lung, colorectal, and pancreatic cancers, yet its direct inhibition remains challenging due to its smooth surface and absence of deep binding pockets. While covalent inhibitors such as sotorasib (now an approved therapy) have demonstrated promise targeting KRAS-G12C, and inhibitors such as MRTX1133 are undergoing clinical development for KRAS-G12D, signi fi cant hurdles remain in achieving broad-spectrum inhibition across multiple KRAS variants, including G12V. 2 This highlights the urgent need for complementary strategies capable of expanding the druggable landscape. The study used a QCBM, a quantum generative model that employs quantum superposition and entanglement to ef fi ciently sample complex molecular distributions. Integrated with an LSTM network, a powerful deep-learning algorithm frequently used for sequential data modeling, this hybrid computational approach enables the de…
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