Quantum biological convergence: quantum computing accelerates KRAS inhibitor design
Taeho Kwon, Hak‐Jin Kim
Abstract
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. 1 This research highlights how quantum-enhanced AI (artificial intelligence), when supported by substantial pre-existing data, can contribute to the discovery of inhibitors for challenging targets such as KRAS. 1
Topics & Concepts
KRASConvergence (economics)Computational biologyComputer scienceCancer researchMedicineBiologyGeneticsCancerEconomic growthEconomicsColorectal cancerComputational Drug Discovery MethodsReceptor Mechanisms and SignalingMonoclonal and Polyclonal Antibodies Research