BioPhytMol: a drug discovery community resource on anti-mycobacterial phytomolecules and plant extracts

Arun Sharma; Prasun Dutta; Maneesh Sharma; Neeraj Kumar Rajput; Bhavna Dodiya; John J Georrge; Trupti Kholia; OSDD Consortium; Anshu Bhardwaj

doi:10.1186/s13321-014-0046-2

BioPhytMol: a drug discovery community resource on anti-mycobacterial phytomolecules and plant extracts

J Cheminform. 2014 Oct 11;6(1):46. doi: 10.1186/s13321-014-0046-2. eCollection 2014 Dec.

Authors

Arun Sharma¹, Prasun Dutta¹, Maneesh Sharma², Neeraj Kumar Rajput¹, Bhavna Dodiya³, John J Georrge⁴, Trupti Kholia⁵; OSDD Consortium; Anshu Bhardwaj¹

Affiliations

¹ Open Source Drug Discovery (OSDD) Unit, Council of Scientific and Industrial Research, New Delhi, India.
² St. Stephens College, University of Delhi, New Delhi, India.
³ Department of Applied Mathematics and Bioinformatics, Faculty of Technology and Engineering, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat India.
⁴ Department of Bioinformatics, Christ College, Rajkot, Gujarat India ; Department of Biochemistry and Molecular Biology, University Clinic of Bonn (UKB), University of Bonn, Bonn, Germany.
⁵ Department of Bioinformatics, Christ College, Rajkot, Gujarat India.

Abstract

Background: Tuberculosis (TB) is the second leading cause of death from a single infectious organism, demanding attention towards discovery of novel anti-tubercular compounds. Natural products or their derivatives have provided more than 50% of all existing drugs, offering a chemically diverse space for discovery of novel drugs.

Description: BioPhytMol has been designed to systematically curate and analyze the anti-mycobacterial natural product chemical space. BioPhytMol is developed as a drug-discovery community resource with anti-mycobacterial phytomolecules and plant extracts. Currently, it holds 2582 entries including 188 plant families (692 genera and 808 species) from global flora, manually curated from literature. In total, there are 633 phytomolecules (with structures) curated against 25 target mycobacteria. Multiple analysis approaches have been used to prioritize the library for drug-like compounds, for both whole cell screening and target-based approaches. In order to represent the multidimensional data on chemical diversity, physiochemical properties and biological activity data of the compound library, novel approaches such as the use of circular graphs have been employed.

Conclusion: BioPhytMol has been designed to systematically represent and search for anti-mycobacterial phytochemical information. Extensive compound analyses can also be performed through web-application for prioritizing drug-like compounds. The resource is freely available online at http://ab-openlab.csir.res.in/biophytmol/. Graphical AbstractBioPhytMol: a drug discovery community resource on anti-mycobacterial phytomolecules and plant extracts generated using Crowdsourcing. The platform comprises of manually curated data on antimycobacterial natural products along with tools to perform structure similarity and visualization. The platform allows for prioritization of drug like natural products for antimycobacterial drug discovery.

Keywords: Anti-TB; Anti-mycobacterial; Anti-tubercular; BioPhytMol; Crowdsourcing; Drug discovery; TB; Tuberculosis.