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Target Identification by Enzymes (TIE) problem aims to identify the set ofenzymes in a given metabolic network, such that their inhibition eliminates agiven set of target compounds associated with a disease while incurring minimumdamage to the rest of the compounds. This is an NP-complete problem, and thusoptimal solutions using classical computers fail to scale to large metabolicnetworks. In this paper, we consider the TIE problem for identifying drugtargets in metabolic networks. We develop the first quantum optimizationsolution, called QuTIE (Quantum optimization for Target Identification byEnzymes), to this NP-complete problem. We do that by developing an equivalentformulation of the TIE problem in Quadratic Unconstrained Binary Optimization(QUBO) form, then mapping it to a logical graph, which is then embedded on ahardware graph on a quantum computer. Our experimental results on 27 metabolicnetworks from Escherichia coli, Homo sapiens, and Mus musculus show that QuTIEyields solutions which are optimal or almost optimal. Our experiments alsodemonstrate that QuTIE can successfully identify enzyme targets alreadyverified in wet-lab experiments for 14 major disease classes.

QuTIE: Quantum optimization for Target Identification by Enzymes