An evolutionary approach to drug-design using quantam binary particle swarm optimization algorithm

Avishek Ghosh, Arnab Ghosh, Arkabandhu Chowdhury, Jubin Hazra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The present work provides a new approach to evolve ligand structures which represent possible drug to be docked to the active site of the target protein. The structure is represented as a tree where each non-empty node represents a functional group. It is assumed that the active site configuration of the target protein is known with position of the essential residues. In this paper the interaction energy of the ligands with the protein target is minimized. Moreover, the size of the tree is difficult to obtain and it will be different for different active sites. To overcome the difficulty, a variable tree size configuration is used for designing ligands. The optimization is done using a quantum discrete PSO. The result using fixed length and variable length configuration are compared.

Original languageEnglish (US)
Title of host publication2012 IEEE Students' Conference on Electrical, Electronics and Computer Science
Subtitle of host publicationInnovation for Humanity, SCEECS 2012
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 IEEE Students' Conference on Electrical, Electronics and Computer Science: Innovation for Humanity, SCEECS 2012 - Bhopal, India
Duration: Mar 1 2012Mar 2 2012

Publication series

Name2012 IEEE Students' Conference on Electrical, Electronics and Computer Science: Innovation for Humanity, SCEECS 2012

Conference

Conference2012 IEEE Students' Conference on Electrical, Electronics and Computer Science: Innovation for Humanity, SCEECS 2012
Country/TerritoryIndia
CityBhopal
Period3/1/123/2/12

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Electrical and Electronic Engineering

Keywords

  • Quantum discrete PSO
  • Van der Wools energy
  • Variable length structure
  • ligand docking
  • proteins
  • tree representation

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