Computational Drug Repositioning: From Data to Therapeutics

By M. Hurle, Lun Yang, Qing Xie, D. Rajpal, Philippe Sanseau, Pankaj Agarwal. 2013

340 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

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Citations in the corpus

SWEETLEAD: an In Silico Database of Approved Drugs, Regulated Chemicals, and Herbal Isolates for Computer-Aided Drug Discovery (2013, neutral): “promising new therapeutic strategies[15-20].”
Molecular Docking: Shifting Paradigms in Drug Discovery (2019, background): “Drug repositioning, or repurposing, represents an established drug discovery approach that allows identifying novel therapeutic uses for already approved drugs, candidate compounds under clinical evaluation, natural products, or already synthesized ligands in general [178].”
Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory (2016, methodology): “…repositioned drugs have been based on various computational methods, such as mapping gene expression profiles using drug response profiles [10 – 14], the use of side-effect-based similarities [15 – 17], heterogeneous network clustering [18], and the graph-based inference method [19 – 22].”
Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature (2014, background): “Similarly, many computational strategies for drug repositioning have been explored [14].”
Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm (2016, methodology): “The gold standard dataset used in this study is obtained from the Supplementary Material of paper Gottlieb et al. (2011), which collected comprehensive associations between drugs and diseases from multiple data sources.”
Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task (2016, background): “Greater than 95% of potential drugs fail during development for reasons such as undesired side effects due to either off-target binding or unanticipated physiologic roles of the intended target (4).”
Systematic integration of biomedical knowledge prioritizes drugs for repurposing (2017, background): “Modern computational approaches offer a convenient platform to tie these developments together as the reduced cost and increased velocity of in silico experimentation massively lowers the barriers to entry and price of failure ( Hurle et al., 2013 ; Liu et al., 2013 ).”