Integrated machine learning, molecular docking and 3D-QSAR based approach for identification of potential inhibitors of trypanosomal N-myristoyltransferase-Reference-Cited by-同舟云学术

Integrated machine learning, molecular docking and 3D-QSAR based approach for identification of potential inhibitors of trypanosomal N-myristoyltransferase

Published:2016 Issue:12 Volume:12 Page:3711-3723
ISSN:1742-206X
Container-title:Molecular BioSystems
language:en
Short-container-title:Mol. BioSyst.

Author:

Singh Nidhi¹²³⁴,Shah Priyanka¹²³⁴,Dwivedi Hemlata⁵²⁶⁴,Mishra Shikha¹²³⁴,Tripathi Renu⁵²⁶⁴⁷,Sahasrabuddhe Amogh A.¹²³⁴⁷,Siddiqi Mohammad Imran¹²³⁴⁷

Affiliation:

1. Molecular and Structural Biology Division

2. CSIR-Central Drug Research Institute

3. Lucknow 226031

4. India

5. Division of Parasitology

6. Lucknow

7. Academy of Scientific and Innovative Research (AcSIR)

Abstract

Integrated in silico approaches for the identification of antitrypanosomal inhibitors.

Publisher

Royal Society of Chemistry (RSC)

Subject

Molecular Biology,Biotechnology

Link

http://pubs.rsc.org/en/content/articlepdf/2016/MB/C6MB00574H

Reference47 articles.

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3. Leishmaniasis: Current status of available drugs and new potential drug targets

4. http://www.who.int/mediacentre/factsheets/fs259/en/, accessed on March 30, 2016

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