Paper Info
Title
Multi-objective ligand-protein docking with particle swarm optimizers.
Abstract
In the last years, particle swarm optimizers have emerged as prominent search methods to solve the molecular docking problem. A new approach to address this problem consists in a multi-objective formulation, minimizing the intermolecular energy and the Root Mean Square Deviation (RMSD) between the atom coordinates of the co-crystallized and the predicted ligand conformations. In this paper, we analyze the performance of a set of multi-objective particle swarm optimization variants based on different archiving and leader selection strategies, in the scope of molecular docking. The conducted experiments involve a large set of 75 molecular instances from the Protein Data Bank database (PDB) characterized by different sizes of HIV-protease inhibitors. The main motivation is to provide molecular biologists with unbiased conclusions concerning which algorithmic variant should be used in drug discovery. Our study confirms that the multi-objective particle swarm algorithms SMPSOhv and MPSO/D show the best overall performance. An analysis of the resulting molecular ligand conformations, in terms of binding site and molecular interactions, is also performed to validate the solutions found, from a biological point of view.
Year
DOI
Venue
2019
10.1016/j.swevo.2018.05.007
Swarm and Evolutionary Computation
Keywords
Field
DocType
Multi-objective optimization,Particle swarm optimization,Molecular docking,Archiving strategies,Algorithm comparison
Particle swarm optimization,Docking (molecular),Drug discovery,Biological system,Computer science,Macromolecular docking,Root-mean-square deviation,Protein Data Bank,Intermolecular force,Protein Data Bank (RCSB PDB)
Journal
Volume
ISSN
Citations 
44
2210-6502
1
PageRank 
References 
Authors
0.35
21
5
Name
Order
Citations
PageRank
José García-Nieto134825.75
Esteban López-Camacho2295.13
María Jesús García-Godoy3345.57
Antonio J. Nebro4111854.62
José Francisco Aldana Montes527636.81