# Automatic hyperparameter selection in Autodock

## TL;DR

This paper presents an automatic hyperparameter tuning method for AutoDock, using a surrogate-based multi-objective algorithm to improve conformation search efficiency and assist novice users.

## Contribution

It introduces a surrogate-based multi-objective optimization approach for automatic hyperparameter tuning in AutoDock, enhancing usability and search performance.

## Key findings

- The method effectively tunes hyperparameters for AutoDock.
- Experimental results show improved search efficiency.
- The approach is practical and beneficial for users.

## Abstract

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.