An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19
Table 1
Molecular docking tools for protein-ligand interaction studies.
Tools
Key features
Reference
AutoDock
The methods available for conformational searching in AutoDock are Lamarckian genetic algorithm, simulated annealing search, and a traditional genetic algorithm search. The prediction of binding free energies of small molecules to protein targets is based on a semiempirical free energy force field
AutoDock Vina calculations rely on a sophisticated gradient optimization method and achieve approximately two orders of magnitude improvement in speed and better accuracy of predicting binding modes compared to AutoDock
GOLD (genetic optimization for ligand docking) is an automated ligand docking program that allows full ligand conformational flexibility with partial flexibility of the protein and explores the binding conformations using a genetic algorithm
CDOCKER (CHARMm-based DOCKER) is an automated MD docking program that uses the CHARMm19 family of force fields and offers full flexibility of ligand and CHARMm engine with reduced computation time
FLEXX is a full automated docking tool for flexible ligands which produces reliable results with good accuracy. The FlexX method is dependent on the selection and placement of base fragments of ligand and placement and the assumption that the best base fragments interacting with the active site give a good score
Surflex is a docking program that uses a combination of combined Hammerhead’s empirical scoring function and molecular similarity method to produce putative poses of ligand fragments
Glide (grid-based ligand docking with energetics) performs an exhaustive search of the positional, orientational, and conformational space of a ligand binding to a receptor with reasonable computational speed. The scoring of the binding conformations is based on the ChemScore function.
