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• W4387664865 abstract "Abstract Prediction of tautomers plays an essential role in computer-aided drug discovery. However, it remains a challenging task nowadays to accurately predict the canonical tautomeric form of a given drug-like molecule. Lack of extensive tautomer databases, most likely due to the difficulty in experimental studies, hampers the development of effective empirical methods for tautomer predictions. A more accurate estimation of the stable tautomeric form can be achieved by quantum chemistry calculations. Yet, the computational cost required prevents quantum chemistry calculation as a standard tool for tautomer prediction in computer-aided drug discovery. In this paper we propose a hybrid quantum chemistry-quantum computation workflow to efficiently predict the dominant tautomeric form. Specifically, we select active-space molecular orbitals based on quantum chemistry methods. Then we utilize efficient encoding methods to map the Hamiltonian onto quantum devices to reduce the qubit resources and circuit depth. Finally, variational quantum eigensolver (VQE) algorithms are employed for ground state estimation where hardware-efficient ansatz circuits are used. To demonstrate the applicability of our methodology, we perform experiments on two tautomeric systems: acetone and Edaravone, each having 52 and 150 spin-orbitals in the Slater Type Orbital - 3 Gaussian (STO-3G) basis set, respectively. Our numerical results show that their tautomeric state prediction agrees with the Coupled Cluster Singles and Doubles (CCSD) benchmarks. Moreover, the required quantum resources are efficient: in the example of Edaravone, we could achieve chemical accuracy with only eight qubits and 80 two-qubit gates." @default.
• W4387664865 created "2023-10-17" @default.
• W4387664865 creator A5007297955 @default.
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• W4387664865 date "2023-10-16" @default.
• W4387664865 modified "2023-10-17" @default.
• W4387664865 title "Quantum simulation of preferred tautomeric state prediction" @default.
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• W4387664865 doi "https://doi.org/10.1038/s41534-023-00767-9" @default.
• W4387664865 hasPublicationYear "2023" @default.
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