Literature DB >> 15032724

Virtual screening for kinase targets.

Ingo Muegge1, Istvan J Enyedy.   

Abstract

Kinases have become a major area of drug discovery and structure-based design. Hundreds of 3D structures for more than thirty different kinases are available to the public. High structural and sequence homology within the kinase gene family makes the remaining kinases ideal targets for homology modeling and virtual screening. Somewhat surprisingly, however, the number of publications about virtual screening of kinases is very low. Therefore, rather than reviewing the field of virtual screening for kinases, we attempt here a hybrid approach of presenting what is known and common practice together with new studies on CDK2 and SRC kinase. To illustrate the challenges and pitfalls of virtual screening for kinase targets we focus on the question of how ranking is influenced by the database screened, the docking scheme, the scoring function, the activity of the compounds used for testing, and small changes in the binding pocket. In addition, a case study of finding irreversible inhibitors of ErbB2 through in silico screening is presented.

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Year:  2004        PMID: 15032724     DOI: 10.2174/0929867043455684

Source DB:  PubMed          Journal:  Curr Med Chem        ISSN: 0929-8673            Impact factor:   4.530


  16 in total

1.  Chemical space sampling by different scoring functions and crystal structures.

Authors:  Natasja Brooijmans; Christine Humblet
Journal:  J Comput Aided Mol Des       Date:  2010-04-18       Impact factor: 3.686

2.  Biased retrieval of chemical series in receptor-based virtual screening.

Authors:  Natasja Brooijmans; Jason B Cross; Christine Humblet
Journal:  J Comput Aided Mol Des       Date:  2010-10-30       Impact factor: 3.686

3.  Targeting plague virulence factors: a combined machine learning method and multiple conformational virtual screening for the discovery of Yersinia protein kinase A inhibitors.

Authors:  Xin Hu; Gerd Prehna; C Erec Stebbins
Journal:  J Med Chem       Date:  2007-08-03       Impact factor: 7.446

Review 4.  In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling.

Authors:  S Ekins; J Mestres; B Testa
Journal:  Br J Pharmacol       Date:  2007-06-04       Impact factor: 8.739

5.  Multiple protein structures and multiple ligands: effects on the apparent goodness of virtual screening results.

Authors:  Robert P Sheridan; Georgia B McGaughey; Wendy D Cornell
Journal:  J Comput Aided Mol Des       Date:  2008-02-14       Impact factor: 3.686

6.  Can we use docking and scoring for hit-to-lead optimization?

Authors:  Istvan J Enyedy; William J Egan
Journal:  J Comput Aided Mol Des       Date:  2008-01-09       Impact factor: 3.686

Review 7.  A structure-function perspective of Jak2 mutations and implications for alternate drug design strategies: the road not taken.

Authors:  K Gnanasambandan; P P Sayeski
Journal:  Curr Med Chem       Date:  2011       Impact factor: 4.530

8.  The VSGB 2.0 model: a next generation energy model for high resolution protein structure modeling.

Authors:  Jianing Li; Robert Abel; Kai Zhu; Yixiang Cao; Suwen Zhao; Richard A Friesner
Journal:  Proteins       Date:  2011-08-22

9.  Evaluation of the utility of homology models in high throughput docking.

Authors:  Philippe Ferrara; Edgar Jacoby
Journal:  J Mol Model       Date:  2007-05-09       Impact factor: 1.810

10.  Machine learning methods and docking for predicting human pregnane X receptor activation.

Authors:  Akash Khandelwal; Matthew D Krasowski; Erica J Reschly; Michael W Sinz; Peter W Swaan; Sean Ekins
Journal:  Chem Res Toxicol       Date:  2008-06-12       Impact factor: 3.739

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