title: Computational polypharmacology: a new paradigm for drug discovery.
abstract: Over the past couple of years, the cost of drug development has sharply increased along with the high rate of clinical trial failures. Such increase in expenses is partially due to the inability of the "one drug - one target" approach to predict drug side effects and toxicities. To tackle this issue, an alternative approach, known as polypharmacology, is being adopted to study small molecule interactions with multiple targets. Apart from developing more potent and effective drugs, this approach allows for studies of off-target activities and the facilitation of drug repositioning. Although exhaustive polypharmacology studies in-vitro or in-vivo are not practical, computational methods of predicting unknown targets or side effects are being developed. Areas covered: This article describes various computational approaches that have been developed to study polypharmacology profiles of small molecules. It also provides a brief description of the algorithms used in these state-of-the-art methods. Expert opinion: Recent success in computational prediction of multi-targeting drugs has established polypharmacology as a promising alternative approach to tackle some of the daunting complications in drug discovery. This will not only help discover more effective agents, but also present tremendous opportunities to study novel target pharmacology and facilitate drug repositioning efforts in the pharmaceutical industry.
authors.affiliation: a Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics , The University of Texas MD Anderson Cancer Center , Houston , TX , USA.
authors.forename: Rajan
authors.initials: R
authors.lastname: Chaudhari
authors.affiliation: a Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics , The University of Texas MD Anderson Cancer Center , Houston , TX , USA.|b The University of Texas Graduate School of Biomedical Sciences , Houston , TX , USA.
authors.forename: Zhi
authors.initials: Z
authors.lastname: Tan
authors.affiliation: a Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics , The University of Texas MD Anderson Cancer Center , Houston , TX , USA.
authors.forename: Beibei
authors.initials: B
authors.lastname: Huang
authors.affiliation: a Integrated Molecular Discovery Laboratory, Department of Experimental Therapeutics , The University of Texas MD Anderson Cancer Center , Houston , TX , USA.|b The University of Texas Graduate School of Biomedical Sciences , Houston , TX , USA.
authors.forename: Shuxing
authors.initials: S
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D015195:Drug Design; D055808:Drug Discovery / Q000379:methods*; D004345:Drug Industry / Q000379:methods; D058492:Drug Repositioning / Q000379:methods; D064420:Drug-Related Side Effects and Adverse Reactions / Q000175:diagnosis; D006801:Humans; D064798:Polypharmacology
keywords: Drug polypharmacology; computer-aided drug design; drug repurposing; in silico prediction; multi-targeting ligands