Automatic hyperparameter selection in Autodock
This work addresses the difficulty for novice users in configuring hyperparameters in Autodock, though it is incremental as it builds on existing methods.
The authors tackled the problem of hyperparameter selection in Autodock, a molecular modeling tool, by designing a surrogate-based multi-objective algorithm that automatically tunes settings, resulting in a practical and effective component as shown in experiments.
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.