Parallel multi-objective algorithms for the molecular docking problem

TL;DR

This paper introduces a multi-objective model for molecular docking that optimizes for both energy and surface area, validated with genetic algorithms on benchmark instances.

Contribution

It presents a novel multi-objective model combining energy and surface terms for molecular docking, validated with genetic algorithms on benchmark datasets.

Findings

The model effectively balances energy and surface area in docking complexes.

Genetic algorithms successfully optimize the proposed multi-objective model.

Validation on benchmark instances demonstrates the model's practical utility.

Abstract

Molecular docking is an essential tool for drug design. It helps the scientist to rapidly know if two molecules, respectively called ligand and receptor, can be combined together to obtain a stable complex. We propose a new multi-objective model combining an energy term and a surface term to gain such complexes. The aim of our model is to provide complexes with a low energy and low surface. This model has been validated with two multi-objective genetic algorithms on instances from the literature dedicated to the docking benchmarking.