Susan Gregurick (@sgregurick.bsky.social)

Quantum Computing Calculations of Protein–Ligand Binding Energies Using Decomposition Methods on Simulated and Real Quantum Hardware | Journal of Chemical Information and Modeling pubs.acs.org/doi/10.1021/... 🧪 quantum algorithms can enhance the binding energy correlation value.

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Quantum Computing Calculations of Protein–Ligand Binding Energies Using Decomposition Methods on Simulated and Real Quantum Hardware We analyzed the performance of quantum algorithms for studying protein–ligand systems, using both simulator and real hardware. To achieve this, we selected thrombin and 5 ligands as a test case and employed a decomposition strategy using density matrix embedded theory, dividing the ligand systems into three fragments each. First, the energy of one of the fragments was calculated using the Variational Quantum Eigensolver (VQE) using a state vector simulator, while the energy of the remaining fragments was calculated using Couple Cluster Singles and Doubles (CCSD), with the protein treated as point charges located at the respective atomic sites. Through these noiseless simulations, we evaluated the results under ideal conditions using the state-of-the-art Unitary CCSD ansatz to validate the efficacy of the strategy in a controlled setting without quantum noise. Subsequently, we evaluated the impact of approximations in the ansatz, optimizer, and active space size, which are necessary to decrease the computational cost, in order to target real hardware during the current Noisy Intermediate-Scale Quantum era. Finally, we performed the VQE calculations using a superconducting quantum computer developed by the RIKEN RQC–Fujitsu Collaboration Center and also analyzed the noise effect through simulations. The results demonstrated that the use of quantum algorithms can enhance the binding energy correlation value, offering potential applications in the workflow of computer-aided drug design. https://pubs.acs.org/doi/10.1021/acs.jcim.6c00544