ICM—A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation

By Ruben Abagyan, Maxim Totrov, D. Kuznetsov. 1994

Subfields: Computational Biology & Bioinformatics

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Citations in the corpus

Molecular Docking and Structure-Based Drug Design Strategies (2015, methodology): “…Knowledge-Based DOCK [32] AutoDock [34] SMoG [82] AutoDock [34] GlideScore [37] DrugScore [62] GoldScore [35] ChemScore [60] PMF_Score [83] ICM [46] X_Score [66] MotifScore [84] LigandFit [47] F_Score [73] RF_Score [85] Molegro Virtual Docker [48] Fresno [75] PESD_SVM [86] SYBYL_G-Score [73]…”
Rosetta3: An Object-Oriented Software Suite for the Simulation and Design of Macromolecules (2011, neutral): “The most commonly used class is AtomTreeMinimizer .”
High quality binding modes in docking ligands to proteins (2008, background): “Such explorations are typical of a few programs such as ICM,29 Glide,30 AutoDock, 31–33 and GOLD.”
Docking and scoring with ICM: the benchmarking results and strategies for improvement (2012, methodology): “Flexible ligand docking with the ICM software uses Monte Carlo simulations to globally optimize a set of ligand internal coordinates in the space of grid potential maps calculated for the protein pocket [39], according to the following procedure: (1) a random move is introduced to one of the rotatio”
GAMESS as a free quantum-mechanical platform for drug research. (2012, methodology): “Next, receptor structure can be docked with ligand using one of the docking programs like Autodock [174], Autodock Vina [175], Dock [176], GOLD [177, 178], FlexX [179], FRED [180], Surflex [181], GLIDE [182, 183], and ICM [184] to generate binding poses.”
Protein flexibility predictions using graph theory (2001, background): “22 In addition to covalent bonding forces, hydrogen bonds (Fig.”
Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock (2009, methodology): “Discussion The development of docking software that is able to accurately predict binding geometry and binding energy represents a great challenge in computational chemistry [68,71-73].”
Pocketome via Comprehensive Identification and Classification of Ligand Binding Envelopes* (2005, methodology): “The algorithm was coded with the ICM1 scripting language ( 28 , 29).”