Literature DB >> 10637360

Molecular modeling and computer aided drug design. Examples of their applications in medicinal chemistry.

F Ooms1.   

Abstract

The development of new drugs with potential therapeutic applications is one of the most complex and difficult process in the pharmaceutical industry. Millions of dollars and man-hours are devoted to the discovery of new therapeutical agents. As, the activity of a drug is the result of a multitude of factors such as bioavailability, toxicity and metabolism, rational drug design has been utopias for centuries. Very recently, impressive technological advances in areas such as structural characterization of biomacromolecules, computer sciences and molecular biology have made rational drug design feasible. The aim of this review is to give an outline of studies in the field of medicinal chemistry in which molecular modeling has helped in the discovery process of new drugs. The emphasis will be on lead generation and optimization.

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Year:  2000        PMID: 10637360     DOI: 10.2174/0929867003375317

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


  14 in total

1.  Analogue-based approaches in anti-cancer compound modelling: the relevance of QSAR models.

Authors:  Mohammed Hussaini Bohari; Hemant Kumar Srivastava; Garikapati Narahari Sastry
Journal:  Org Med Chem Lett       Date:  2011-07-18

2.  Computer-aided drug design platform using PyMOL.

Authors:  Markus A Lill; Matthew L Danielson
Journal:  J Comput Aided Mol Des       Date:  2010-10-30       Impact factor: 3.686

3.  Hierarchical QSAR technology based on the Simplex representation of molecular structure.

Authors:  V E Kuz'min; A G Artemenko; E N Muratov
Journal:  J Comput Aided Mol Des       Date:  2008-02-06       Impact factor: 3.686

4.  Testing and validation of the Automated Topology Builder (ATB) version 2.0: prediction of hydration free enthalpies.

Authors:  Katarzyna B Koziara; Martin Stroet; Alpeshkumar K Malde; Alan E Mark
Journal:  J Comput Aided Mol Des       Date:  2014-01-30       Impact factor: 3.686

5.  Rapid activity prediction of HIV-1 integrase inhibitors: harnessing docking energetic components for empirical scoring by chemometric and artificial neural network approaches.

Authors:  Patcharapong Thangsunan; Sila Kittiwachana; Puttinan Meepowpan; Nawee Kungwan; Panchika Prangkio; Supa Hannongbua; Nuttee Suree
Journal:  J Comput Aided Mol Des       Date:  2016-06-17       Impact factor: 3.686

6.  Comparative analysis of L-sorbose dehydrogenase by docking strategy for 2-keto-L-gulonic acid production in Ketogulonicigenium vulgare and Bacillus endophyticus consortium.

Authors:  Si Chen; Nan Jia; Ming-Zhu Ding; Ying-Jin Yuan
Journal:  J Ind Microbiol Biotechnol       Date:  2016-08-26       Impact factor: 3.346

7.  Conditional probabilistic analysis for prediction of the activity landscape and relative compound activities.

Authors:  Radleigh G Santos; Marc A Giulianotti; Richard A Houghten; José L Medina-Franco
Journal:  J Chem Inf Model       Date:  2013-09-17       Impact factor: 4.956

8.  Bioinformatics: A rational combine approach used for the identification and in-vitro activity evaluation of potent β-Glucuronidase inhibitors.

Authors:  Maria Yousuf; Nimra Naveed Shaikh; Zaheer Ul-Haq; M Iqbal Choudhary
Journal:  PLoS One       Date:  2018-12-05       Impact factor: 3.240

9.  Sanjeevini: a freely accessible web-server for target directed lead molecule discovery.

Authors:  B Jayaram; Tanya Singh; Goutam Mukherjee; Abhinav Mathur; Shashank Shekhar; Vandana Shekhar
Journal:  BMC Bioinformatics       Date:  2012-12-13       Impact factor: 3.169

10.  Developing and validating predictive decision tree models from mining chemical structural fingerprints and high-throughput screening data in PubChem.

Authors:  Lianyi Han; Yanli Wang; Stephen H Bryant
Journal:  BMC Bioinformatics       Date:  2008-09-25       Impact factor: 3.169

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