Molecular docking via weighted subgraph isomorphism on quantum annealers

Abstract Molecular docking is an essential step in the drug discovery process involving the detection of three-dimensional poses of a ligand inside the active site of the protein. In this paper, we address the Molecular Docking search phase by formulating the problem in quadratic unconstrained binary optimization terms, suitable for an annealing approach. We propose a problem formulation as a weighted subgraph isomorphism between the ligand graph and the grid of the target protein pocket. In particular, we applied a graph representation to the ligand embedding all the geometrical properties of the molecule including its flexibility, and we created a weighted spatial grid to the 3D space region inside the pocket. The proposed quantum annealing-based method for molecular docking achieves valid ligand placements. Compared to simulated annealing, quantum solvers sampled fewer but higher-quality solutions with lower root-mean-square deviation, demonstrating competitive performance within hardware limits.