  Autodock is a widely used molecular modeling tool which predicts how small
molecules bind to a receptor of known 3D structure. The current version of
AutoDock uses meta-heuristic algorithms in combination with local search
methods for doing the conformation search. Appropriate settings of
hyperparameters in these algorithms are important, particularly for novice
users who often find it hard to identify the best configuration. In this work,
we design a surrogate based multi-objective algorithm to help such users by
automatically tuning hyperparameter settings. The proposed method iteratively
uses a radial basis function model and non-dominated sorting to evaluate the
sampled configurations during the search phase. Our experimental results using
Autodock show that the introduced component is practical and effective.
