Title: Swarm intelligence for molecular docking

Authors: Yu Liu; Lei Zhao; Mingli Li; Changfei Zhao

Addresses: School of Software, Dalian University of Technology, Dalian 116023, China ' School of Software, Dalian University of Technology, Dalian 116023, China ' School of Software, Dalian University of Technology, Dalian 116023, China ' School of Software, Dalian University of Technology, Dalian 116023, China

Abstract: Molecular docking plays an important role in the quest for potential drug candidates, which is an extremely complex high-dimensional optimisation problem. Swarm intelligence comes from swarming behaviours of groups of organisms, which can form a powerful collective intelligence to solve complex problems through the interaction mechanism among individuals in the colony. This character determines that swarm intelligence has a large potential in dealing with molecular docking problem. In this paper, we designed a unified framework based on swarm intelligence for docking problems. And then we have tried to implement three novel swarm intelligence-based docking programmes under the unified framework, called as FIPSDock, ABCDock and AFSADock, which adopt the fully informed particle swarm (FIPS) optimisation algorithm, the mutual artificial bee colony (MABC) algorithm and the simplified artificial fish swarm algorithm (SAFSA) separately. The cognate docking and cross-docking experiments reveal that swarm intelligence-based docking programmes take precedence over a few docking programmes including AutoDock, Dock, FlexX and GOLD, in terms of search ability and docking accuracy. The results demonstrate that swarm intelligent optimisation algorithms might be more suitable than the conventional GA-based algorithm for dealing with docking problems and therefore for use in the course of drug design and high-throughput virtual screening.

Keywords: molecular docking; swarm intelligence; cross-docking; virtual screening; potential drug candidates; optimisation; drug discovery; particle swarm optimisation; PSO; artificial bee colony; ABC; artificial fish swarm algorithm; AFSA; drug design.

DOI: 10.1504/IJMIC.2013.053541

International Journal of Modelling, Identification and Control, 2013 Vol.18 No.4, pp.357 - 363

Published online: 29 Apr 2013 *

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