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The term "docking" originated in the late 1970s, with a more restricted meaning; then, "docking" meant refining a model of a complex structure by optimizing the separation between the interactors but keeping their relative orientations fixed. Later, the relative orientations of the interacting partners in the modelling was allowed to vary, but ...
www.schrodinger.com /products /glide Glide is a molecular modeling software for docking of small molecules into proteins and other biopolymers . [ 1 ] [ 2 ] It was developed by Schrödinger, Inc.
During the course of the docking process, the ligand and the protein adjust their conformation to achieve an overall "best-fit" and this kind of conformational adjustment resulting in the overall binding is referred to as "induced-fit". [5] Molecular docking research focuses on computationally simulating the molecular recognition process.
The number of notable protein-ligand docking programs currently available is high and has been steadily increasing over the last decades. The following list presents an overview of the most common notable programs, listed alphabetically, with indication of the corresponding year of publication, involved organisation or institution, short description, availability of a webservice and the license.
Docking glossary Receptor or host or lock The "receiving" molecule, most commonly a protein or other biopolymer. Ligand or guest or key The complementary partner molecule which binds to the receptor. Ligands are most often small molecules but could also be another biopolymer. Docking Computational simulation of a candidate ligand binding to a ...
Coarse-grained models are often implemented in the case of protein-peptide docking, as they frequently involve large-scale conformation transitions of the protein receptor. [7] [8] AutoDock is one of the computational tools frequently used to model the interactions between proteins and ligands during the drug discovery process. Although the ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Comprehensive life science modeling and simulation suite of applications focused on optimizing drug discovery process: small molecule simulations, QM-MM, pharmacophore modeling, QSAR, protein-ligand docking, protein homology modeling, sequence analysis, protein-protein docking, antibody modeling, etc. Proprietary, trial available