Literature DB >> 21534921

Molecular docking: a powerful approach for structure-based drug discovery.

Xuan-Yu Meng1, Hong-Xing Zhang, Mihaly Mezei, Meng Cui.   

Abstract

Molecular docking has become an increasingly important tool for drug discovery. In this review, we present a brief introduction of the available molecular docking methods, and their development and applications in drug discovery. The relevant basic theories, including sampling algorithms and scoring functions, are summarized. The differences in and performance of available docking software are also discussed. Flexible receptor molecular docking approaches, especially those including backbone flexibility in receptors, are a challenge for available docking methods. A recently developed Local Move Monte Carlo (LMMC) based approach is introduced as a potential solution to flexible receptor docking problems. Three application examples of molecular docking approaches for drug discovery are provided.

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Year:  2011        PMID: 21534921      PMCID: PMC3151162          DOI: 10.2174/157340911795677602

Source DB:  PubMed          Journal:  Curr Comput Aided Drug Des        ISSN: 1573-4099            Impact factor:   1.606


  106 in total

1.  SMall Molecule Growth 2001 (SMoG2001): an improved knowledge-based scoring function for protein-ligand interactions.

Authors:  Alexey V Ishchenko; Eugene I Shakhnovich
Journal:  J Med Chem       Date:  2002-06-20       Impact factor: 7.446

Review 2.  Molecular recognition and docking algorithms.

Authors:  Natasja Brooijmans; Irwin D Kuntz
Journal:  Annu Rev Biophys Biomol Struct       Date:  2003-01-28

3.  A new method for mapping macromolecular topography.

Authors:  Mihaly Mezei
Journal:  J Mol Graph Model       Date:  2003-03       Impact factor: 2.518

4.  Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.

Authors:  Richard A Friesner; Jay L Banks; Robert B Murphy; Thomas A Halgren; Jasna J Klicic; Daniel T Mainz; Matthew P Repasky; Eric H Knoll; Mee Shelley; Jason K Perry; David E Shaw; Perry Francis; Peter S Shenkin
Journal:  J Med Chem       Date:  2004-03-25       Impact factor: 7.446

Review 5.  Docking and scoring--theoretically easy, practically impossible?

Authors:  B Coupez; R A Lewis
Journal:  Curr Med Chem       Date:  2006       Impact factor: 4.530

6.  Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes.

Authors:  M D Eldridge; C W Murray; T R Auton; G V Paolini; R P Mee
Journal:  J Comput Aided Mol Des       Date:  1997-09       Impact factor: 3.686

7.  Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites.

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Journal:  Chem Biol       Date:  1996-06

8.  Flexible ligand docking using a genetic algorithm.

Authors:  C M Oshiro; I D Kuntz; J S Dixon
Journal:  J Comput Aided Mol Des       Date:  1995-04       Impact factor: 3.686

9.  Automated docking in crystallography: analysis of the substrates of aconitase.

Authors:  D S Goodsell; H Lauble; C D Stout; A J Olson
Journal:  Proteins       Date:  1993-09

10.  Efficient identification of inhibitors targeting the closed active site conformation of the HPRT from Trypanosoma cruzi.

Authors:  D M Freymann; M A Wenck; J C Engel; J Feng; P J Focia; A E Eakin; S P Craig
Journal:  Chem Biol       Date:  2000-12
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  316 in total

1.  Design of e-pharmacophore models using compound fragments for the trans-sialidase of Trypanosoma cruzi: screening for novel inhibitor scaffolds.

Authors:  Bill R Miller; Adrian E Roitberg
Journal:  J Mol Graph Model       Date:  2013-08-16       Impact factor: 2.518

2.  Antioxidant and immunomodulatory activity induced by stevioside in liver damage: In vivo, in vitro and in silico assays.

Authors:  Sael Casas-Grajales; Erika Ramos-Tovar; Esmeralda Chávez-Estrada; Diana Alvarez-Suarez; Erika Hernández-Aquino; Karina Reyes-Gordillo; Carlos M Cerda-García-Rojas; Javier Camacho; Víctor Tsutsumi; M Raj Lakshman; Pablo Muriel
Journal:  Life Sci       Date:  2019-03-16       Impact factor: 5.037

Review 3.  DNA-protein interaction: identification, prediction and data analysis.

Authors:  Abbasali Emamjomeh; Darush Choobineh; Behzad Hajieghrari; Nafiseh MahdiNezhad; Amir Khodavirdipour
Journal:  Mol Biol Rep       Date:  2019-03-26       Impact factor: 2.316

4.  Negative Image-Based Rescoring: Using Cavity Information to Improve Docking Screening.

Authors:  Olli T Pentikäinen; Pekka A Postila
Journal:  Methods Mol Biol       Date:  2021

5.  Negative Image-Based Screening: Rigid Docking Using Cavity Information.

Authors:  Pekka A Postila; Sami T Kurkinen; Olli T Pentikäinen
Journal:  Methods Mol Biol       Date:  2021

6.  Discovery of Mer kinase inhibitors by virtual screening using Structural Protein-Ligand Interaction Fingerprints.

Authors:  C Da; M Stashko; C Jayakody; X Wang; W Janzen; S Frye; D Kireev
Journal:  Bioorg Med Chem       Date:  2015-01-13       Impact factor: 3.641

Review 7.  Microscopic Characterization of Membrane Transporter Function by In Silico Modeling and Simulation.

Authors:  J V Vermaas; N Trebesch; C G Mayne; S Thangapandian; M Shekhar; P Mahinthichaichan; J L Baylon; T Jiang; Y Wang; M P Muller; E Shinn; Z Zhao; P-C Wen; E Tajkhorshid
Journal:  Methods Enzymol       Date:  2016-07-11       Impact factor: 1.600

8.  Creating and virtually screening databases of fluorescently-labelled compounds for the discovery of target-specific molecular probes.

Authors:  Rhiannon L Kamstra; Saedeh Dadgar; John Wigg; Morshed A Chowdhury; Christopher P Phenix; Wely B Floriano
Journal:  J Comput Aided Mol Des       Date:  2014-08-24       Impact factor: 3.686

9.  Improving inverse docking target identification with Z-score selection.

Authors:  Stephanie S Kim; Melanie L Aprahamian; Steffen Lindert
Journal:  Chem Biol Drug Des       Date:  2019-01-02       Impact factor: 2.817

10.  Neural-Network Scoring Functions Identify Structurally Novel Estrogen-Receptor Ligands.

Authors:  Jacob D Durrant; Kathryn E Carlson; Teresa A Martin; Tavina L Offutt; Christopher G Mayne; John A Katzenellenbogen; Rommie E Amaro
Journal:  J Chem Inf Model       Date:  2015-09-04       Impact factor: 4.956

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