Protein Kinases: Docking and Homology Modeling Reliability
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2010/07/27
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Indian UGC (journal)
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Refrences37
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Citations49
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Tiziano Tuccinardi Dipartimento di Scienze Farmaceutiche, Università di Pisa, via Bonanno 6, 56126 Pisa, Italy, Department of Biology, Sbarro Institute for Cancer Research and Molecular Medicine, Center for Biotechnology, College of Science and Technology, Temple University, Philadelphia, Pennsylvania 19122, Dipartimento Farmaco Chimico Tecnologico, Universita‘ degli Studi di Siena, Via De Gasperi 2, 53100 Siena, Italy
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Maurizio Botta Dipartimento di Scienze Farmaceutiche, Università di Pisa, via Bonanno 6, 56126 Pisa, Italy, Department of Biology, Sbarro Institute for Cancer Research and Molecular Medicine, Center for Biotechnology, College of Science and Technology, Temple University, Philadelphia, Pennsylvania 19122, Dipartimento Farmaco Chimico Tecnologico, Universita‘ degli Studi di Siena, Via De Gasperi 2, 53100 Siena, Italy
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Antonio Giordano Dipartimento di Scienze Farmaceutiche, Università di Pisa, via Bonanno 6, 56126 Pisa, Italy, Department of Biology, Sbarro Institute for Cancer Research and Molecular Medicine, Center for Biotechnology, College of Science and Technology, Temple University, Philadelphia, Pennsylvania 19122, Dipartimento Farmaco Chimico Tecnologico, Universita‘ degli Studi di Siena, Via De Gasperi 2, 53100 Siena, Italy
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Adriano Martinelli Dipartimento di Scienze Farmaceutiche, Università di Pisa, via Bonanno 6, 56126 Pisa, Italy, Department of Biology, Sbarro Institute for Cancer Research and Molecular Medicine, Center for Biotechnology, College of Science and Technology, Temple University, Philadelphia, Pennsylvania 19122, Dipartimento Farmaco Chimico Tecnologico, Universita‘ degli Studi di Siena, Via De Gasperi 2, 53100 Siena, Italy
Cite
Tuccinardi, Tiziano, et al. “Protein Kinases: Docking and Homology Modeling Reliability”. Journal of Chemical Information and Modeling, vol. 50, no. 8, 2010, pp. 1432-41, https://doi.org/10.1021/ci100161z.
Tuccinardi, T., Botta, M., Giordano, A., & Martinelli, A. (2010). Protein Kinases: Docking and Homology Modeling Reliability. Journal of Chemical Information and Modeling, 50(8), 1432-1441. https://doi.org/10.1021/ci100161z
Tuccinardi, Tiziano, Maurizio Botta, Antonio Giordano, and Adriano Martinelli. “Protein Kinases: Docking and Homology Modeling Reliability”. Journal of Chemical Information and Modeling 50, no. 8 (2010): 1432-41. https://doi.org/10.1021/ci100161z.
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Tuccinardi T, Botta M, Giordano A, Martinelli A. Protein Kinases: Docking and Homology Modeling Reliability. Journal of Chemical Information and Modeling. 2010;50(8):1432-41.
Journal Categories
Medicine
Therapeutics
Pharmacology
Science
Chemistry
Science
Chemistry
General
Including alchemy
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Science
Science (General)
Cybernetics
Information theory
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading | Journal of Computational Chemistry |
| 15,158 | 2010 |
| QM/MM As a Tool in Fragment Based Drug Discovery. A Cross-Docking, Rescoring Study of Kinase Inhibitors | Journal of Chemical Information and Modeling |
| 37 | 2009 |
| An Evaluation of Explicit Receptor Flexibility in Molecular Docking Using Molecular Dynamics and Torsion Angle Molecular Dynamics | Journal of Chemical Theory and Computation |
| 35 | 2009 |
| Protein−Ligand Docking against Non-Native Protein Conformers | Journal of Chemical Information and Modeling |
| 111 | 2008 |
| 10.1074/jbc.M609721200 | Journal of Biological Chemistry |
| 2007 |
Refrences Analysis
The category
Science: Biology (General) 19
is the most frequently represented among the references in this article. It primarily includes studies from Journal of Medicinal Chemistry and Journal of Chemical Information and Modeling. The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year
Citations
Citations Analysis
The category
Science: Chemistry 28
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Survey of public domain software for docking simulations and virtual screening and was published in 2011. The most recent citation comes from a 2024 study titled Chalcones As Potent Agents Against Staphylococcus aureus: A Computational Approach. This article reached its peak citation in 2015, with 8 citations. It has been cited in 29 different journals, 13% of which are open access. Among related journals, the Journal of Chemical Information and Modeling cited this research the most, with 12 citations. The chart below illustrates the annual citation trends for this article.