Literature DB >> 21318898

Computer-aided drug discovery and development.

Shuxing Zhang1.   

Abstract

Computer-aided approaches have been widely used in pharmaceutical research to improve the efficiency of the drug discovery and development pipeline. To identify and design small molecules as clinically effective therapeutics, various computational methods have been evaluated as promising strategies, depending on the purpose and systems of interest. Both ligand and structure-based drug design approaches are powerful technologies, which can be applied to virtual screening for lead identification and optimization. Here, we review the progress in this field and summarize the application of some new technologies we developed. These state-of-the-art tools have been used for the discovery and development of active agents for various diseases, in particular for cancer therapies. The described protocols are appropriate for all drug discovery stages, but expertise is still needed to perform the studies based on the targets of interest.

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Year:  2011        PMID: 21318898     DOI: 10.1007/978-1-61779-012-6_2

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  12 in total

Review 1.  Computational methods in drug discovery.

Authors:  Gregory Sliwoski; Sandeepkumar Kothiwale; Jens Meiler; Edward W Lowe
Journal:  Pharmacol Rev       Date:  2013-12-31       Impact factor: 25.468

Review 2.  Computational drug discovery.

Authors:  Si-Sheng Ou-Yang; Jun-Yan Lu; Xiang-Qian Kong; Zhong-Jie Liang; Cheng Luo; Hualiang Jiang
Journal:  Acta Pharmacol Sin       Date:  2012-08-27       Impact factor: 6.150

Review 3.  The oxadiazole antibacterials.

Authors:  Jeshina Janardhanan; Mayland Chang; Shahriar Mobashery
Journal:  Curr Opin Microbiol       Date:  2016-05-27       Impact factor: 7.934

4.  Pharmacological inactivation of Skp2 SCF ubiquitin ligase restricts cancer stem cell traits and cancer progression.

Authors:  Chia-Hsin Chan; John Kenneth Morrow; Chien-Feng Li; Yuan Gao; Guoxiang Jin; Asad Moten; Loren J Stagg; John E Ladbury; Zhen Cai; Dazhi Xu; Christopher J Logothetis; Mien-Chie Hung; Shuxing Zhang; Hui-Kuan Lin
Journal:  Cell       Date:  2013-08-01       Impact factor: 41.582

Review 5.  Artificial Intelligence and the Future of Diagnostic and Therapeutic Radiopharmaceutical Development:: In Silico Smart Molecular Design.

Authors:  Bahar Ataeinia; Pedram Heidari
Journal:  PET Clin       Date:  2021-08-05

6.  A quantitative structure-activity relationship (QSAR) study of some diaryl urea derivatives of B-RAF inhibitors.

Authors:  Sedighe Sadeghian-Rizi; Amirhossein Sakhteman; Farshid Hassanzadeh
Journal:  Res Pharm Sci       Date:  2016-12

7.  Field-Template, QSAR, Ensemble Molecular Docking, and 3D-RISM Solvation Studies Expose Potential of FDA-Approved Marine Drugs as SARS-CoVID-2 Main Protease Inhibitors.

Authors:  Poonam Kalhotra; Veera C S R Chittepu; Guillermo Osorio-Revilla; Tzayhri Gallardo-Velazquez
Journal:  Molecules       Date:  2021-02-10       Impact factor: 4.411

Review 8.  In silico cancer research towards 3R.

Authors:  Claire Jean-Quartier; Fleur Jeanquartier; Igor Jurisica; Andreas Holzinger
Journal:  BMC Cancer       Date:  2018-04-12       Impact factor: 4.430

9.  A Toxicity Prediction Tool for Potential Agonist/Antagonist Activities in Molecular Initiating Events Based on Chemical Structures.

Authors:  Kota Kurosaki; Raymond Wu; Yoshihiro Uesawa
Journal:  Int J Mol Sci       Date:  2020-10-23       Impact factor: 5.923

Review 10.  Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach.

Authors:  Murtala A Ejalonibu; Segun A Ogundare; Ahmed A Elrashedy; Morufat A Ejalonibu; Monsurat M Lawal; Ndumiso N Mhlongo; Hezekiel M Kumalo
Journal:  Int J Mol Sci       Date:  2021-12-09       Impact factor: 5.923

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