Literature DB >> 21843146

Virtual high throughput screening in new lead identification.

Preethi Badrinarayan1, G Narahari Sastry.   

Abstract

Drug discovery continues to be one of the greatest contemporary challenges and rational application of modelling approaches is the first important step to obtain lead compounds, which can be optimised further. Virtual high throughput screening (VHTS) is one of the efficient approaches to obtain lead structures for a given target. Strategic application of different screening filters like pharmacophore mapping, shape-based, ligand-based, molecular similarity etc., in combination with other drug design protocols provide invaluable insights in lead identification and optimization. Screening of large databases using these computational methods provides potential lead compounds, thus triggering a meaningful interplay between computations and experiments. In this review, we present a critical account on the relevance of molecular modelling approaches in general, lead optimization and virtual screening methods in particular for new lead identification. The importance of developing reliable scoring functions for non-bonded interactions has been highlighted, as it is an extremely important measure for the reliability of scoring function. The lead optimization and new lead design has also been illustrated with examples. The importance of employing a combination of general and target specific screening protocols has also been highlighted.

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Year:  2011        PMID: 21843146     DOI: 10.2174/138620711797537102

Source DB:  PubMed          Journal:  Comb Chem High Throughput Screen        ISSN: 1386-2073            Impact factor:   1.339


  8 in total

1.  FDA approved drugs complexed to their targets: evaluating pose prediction accuracy of docking protocols.

Authors:  Mohammed H Bohari; G Narahari Sastry
Journal:  J Mol Model       Date:  2012-05-08       Impact factor: 1.810

2.  Drug repositioning for anti-tuberculosis drugs: an in silico polypharmacology approach.

Authors:  Sita Sirisha Madugula; Selvaraman Nagamani; Esther Jamir; Lipsa Priyadarsinee; G Narahari Sastry
Journal:  Mol Divers       Date:  2021-09-01       Impact factor: 2.943

3.  Discovery of in silico hits targeting the nsP3 macro domain of chikungunya virus.

Authors:  Phuong T V Nguyen; Haibo Yu; Paul A Keller
Journal:  J Mol Model       Date:  2014-04-23       Impact factor: 1.810

4.  Towards systematic exploration of chemical space: building the fragment library module in molecular property diagnostic suite.

Authors:  Anamika Singh Gaur; Lijo John; Nandan Kumar; M Ram Vivek; Selvaraman Nagamani; Hridoy Jyoti Mahanta; G Narahari Sastry
Journal:  Mol Divers       Date:  2022-08-04       Impact factor: 3.364

5.  Mycobacterium tuberculosis Cell Wall Permeability Model Generation Using Chemoinformatics and Machine Learning Approaches.

Authors:  Selvaraman Nagamani; G Narahari Sastry
Journal:  ACS Omega       Date:  2021-06-25

Review 6.  Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence.

Authors:  José T Moreira-Filho; Arthur C Silva; Rafael F Dantas; Barbara F Gomes; Lauro R Souza Neto; Jose Brandao-Neto; Raymond J Owens; Nicholas Furnham; Bruno J Neves; Floriano P Silva-Junior; Carolina H Andrade
Journal:  Front Immunol       Date:  2021-05-31       Impact factor: 7.561

7.  Specificity rendering 'hot-spots' for aurora kinase inhibitor design: the role of non-covalent interactions and conformational transitions.

Authors:  Preethi Badrinarayan; G Narahari Sastry
Journal:  PLoS One       Date:  2014-12-08       Impact factor: 3.240

8.  Lead identification for the K-Ras protein: virtual screening and combinatorial fragment-based approaches.

Authors:  Akbar Ali Khan Pathan; Bhavana Panthi; Zahid Khan; Purushotham Reddy Koppula; Mohammed Saud Alanazi; Narasimha Reddy Parine; Mukesh Chourasia
Journal:  Onco Targets Ther       Date:  2016-05-02       Impact factor: 4.147

  8 in total

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