Table 3.
Different molecular docking tools and their algorithms
| Molecular Docking tool | Algorithm | Website |
|---|---|---|
| AutoDock [77] | Monte Carlo Simulated Annealing, Genetic Algorithm, and Lamarckian Genetic Algorithm | http://autodock.scripps.edu/ |
| Gold [78] | Genetic Algorithm | https://www.ccdc.cam.ac.uk/solutions/csd-discovery/components/gold/ |
| Glide [79] | In-house algorithm using different search criteria and refinement using the Monte Carlo method | https://www.schrodinger.com/products/glide |
| Haddock [80] | In-house algorithm that encodes information from identified or predicted protein interfaces in ambiguous interaction restraints to drive the docking process | https://wenmr.science.uu.nl/ |
| PyDock [81] | Fast protocol which uses electrostatics and desolvation energy to score docking poses generated with FFT-based algorithms | https://life.bsc.es/pid/pydock/ |
| SwissDock [82] | Based on the docking software EADock DSS | http://www.swissdock.ch/ |
| Rosetta [83] | Monte Carlo based multi-scale docking algorithm | https://www.rosettacommons.org/software |
| DOCK [84] | Based in a Geometric Matching Algorithm | http://dock.compbio.ucsf.edu/ |
| DockingServer [85] | Includes the PM6 semi-empirical method to AutoDock | https://www.dockingserver.com/web |
| Medusa Dock [86] | In-house algorithm using a stochastic rotamer library of ligands | https://dokhlab.med.psu.edu/cpi/#/MedusaDock |