  New computational strategies, such as molecular docking, are emerging to
speed up the drug discovery process. This method predicts the activity of
molecules at the binding site of proteins, helping to select the ones that
exhibit desirable behavior and rejecting the rest. However, for large chemical
libraries, it is essential to search and score configurations using fewer
computational resources while maintaining high precision. In this work, we map
the molecular docking problem to a graph problem, a maximum-weight independent
set problem on a unit-disk graph in a physical neutral atom quantum processor.
Here, each vertex represents an atom trapped by optical tweezers. The
Variational Quantum Adiabatic Algorithm (VQAA) approach is used to solve the
generic graph problem with two optimization methods, Scipy and Hyperopt.
Additionally, a machine learning method is explored using the adiabatic
algorithm. Results for multiple graphs are presented, and a small instance of
the molecular docking problem is solved, demonstrating the potential for
near-term quantum applications.
