Balaguru Ravikumar

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Drug-resistant pathogenic fungi use several families of membrane-embedded transporters to efflux antifungal drugs from the cells. The efflux pump Cdr1 (Candida drug resistance 1) belongs to the ATP-binding cassette (ABC) superfamily of transporters. Cdr1 is one of the most predominant mechanisms of multidrug resistance in azole-resistant (AR) clinical(More)
1 Membrane Biology Laboratory, School of Life Sciences, Jawaharlal Nehru University, New Delhi110067, India; 2 International Centre for Genetic Engineering and Biotechnology, New Delhi-110067, India; 3 School of Physical Sciences, Jawaharlal Nehru University, New Delhi-110067, India; 4 School of Computational and Integrative Sciences, Jawaharlal Nehru(More)
INTRODUCTION Polypharmacology has emerged as an essential paradigm for modern drug discovery process. Multiple lines of evidence suggest that agents capable of modulating multiple targets in a selective manner may offer also improved balance between therapeutic efficacy and safety compared to single-targeted agents. Areas covered: Herein, the authors review(More)
  • Jing Tang, Zia-Ur-Rehman Tanoli, +23 authors Tero Aittokallio
  • Cell chemical biology
  • 2017
Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity data resources are often incomparable due to non-standardized and heterogeneous assay types and variability in(More)
The advent of polypharmacology paradigm in drug discovery calls for novel chemoinformatic tools for analyzing compounds' multi-targeting activities. Such tools should provide an intuitive representation of the chemical space through capturing and visualizing underlying patterns of compound similarities linked to their polypharmacological effects. Most of(More)
Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions(More)
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