Combining a genetic algorithm with a linear scaling semiempirical method for protein–ligand docking

2009 ◽  
Vol 898 (1-3) ◽  
pp. 31-41 ◽  
Author(s):  
Eddy Thiriot ◽  
Gerald Monard
Keyword(s):  
Genetic Algorithm ◽  
Semiempirical Method ◽  
Ligand Docking ◽  
Linear Scaling

GANM: A protein–ligand docking approach based on genetic algorithm and normal modes

2012 ◽  
Vol 219 (2) ◽  
pp. 511-520 ◽  
Author(s):  
Angélica Nakagawa Lima ◽  
Eric Allison Philot ◽  
David Perahia ◽  
Antonio Sérgio Kimus Braz ◽  
Luis P.B. Scott
Keyword(s):  
Genetic Algorithm ◽  
Normal Modes ◽  
Ligand Docking ◽  
Docking Approach

scholarly journals HIGA: A Running History Information Guided Genetic Algorithm for Protein–Ligand Docking

Molecules ◽  
2017 ◽  
Vol 22 (12) ◽  
pp. 2233 ◽  
Author(s):  
Boxin Guan ◽  
Changsheng Zhang ◽  
Yuhai Zhao
Keyword(s):  
Genetic Algorithm ◽  
Ligand Docking ◽  
History Information

A biased random key genetic algorithm for the protein–ligand docking problem

2018 ◽  
Vol 23 (12) ◽  
pp. 4155-4176 ◽  
Author(s):  
Pablo Felipe Leonhart ◽  
Eduardo Spieler ◽  
Rodrigo Ligabue-Braun ◽  
Marcio Dorn
Keyword(s):  
Genetic Algorithm ◽  
Ligand Docking

scholarly journals A Hybrid DELGA Algorithm for Protein Ligand Docking

2020 ◽  
Vol 9 (4) ◽  
pp. 2836-2840
Keyword(s):  
Genetic Algorithm ◽  
Molecular Modeling ◽  
Binding Energy ◽  
Research Work ◽  
Ligand Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Lamarckian Genetic Algorithm ◽  
Computational Molecular Modeling ◽  
Docking Energy

Protein-ligand docking is a computational molecular modeling method that is used in drug design to predict the optimal binding pose between the ligand and receptor. AutoDock is an open-source freeware program used to predict docking poses. It uses LGA) Lamarckian genetic algorithm to enumerate the binding energy. In this research work, we proposed an approach of hybrid Differential evolution base Lamarckian genetic (DELGA) algorithm to calculate the lowest binding energy. The experiment conducted to compute the 65 molecular instances, the results exposed that our approach predicts lowest docking energy with minimum root mean square deviation (RMSD) in comparison to the LGA, SA and PSO algorithms.

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