TY - DATA T1 - How good are publicly available web services that predict bioactivity profiles for drug repurposing?$ PY - 2017/11/29 AU - K. A. Murtazalieva AU - D. S. Druzhilovskiy AU - R. K. Goel AU - G. N. Sastry AU - V. V. Poroikov UR - https://tandf.figshare.com/articles/journal_contribution/How_good_are_publicly_available_web_services_that_predict_bioactivity_profiles_for_drug_repurposing_sup_sup_/5644150 DO - 10.6084/m9.figshare.5644150.v1 L4 - https://ndownloader.figshare.com/files/9834142 KW - Drug repurposing KW - bioactivity profile prediction KW - similarity assessment KW - machine learning KW - web services KW - performance evaluation N2 - Drug repurposing provides a non-laborious and less expensive way for finding new human medicines. Computational assessment of bioactivity profiles shed light on the hidden pharmacological potential of the launched drugs. Currently, several freely available computational tools are available via the Internet, which predict multitarget profiles of drug-like compounds. They are based on chemical similarity assessment (ChemProt, SuperPred, SEA, SwissTargetPrediction and TargetHunter) or machine learning methods (ChemProt and PASS). To compare their performance, this study has created two evaluation sets, consisting of (1) 50 well-known repositioned drugs and (2) 12 drugs recently patented for new indications. In the first set, sensitivity values varied from 0.64 (TarPred) to 1.00 (PASS Online) for the initial indications and from 0.64 (TarPred) to 0.98 (PASS Online) for the repurposed indications. In the second set, sensitivity values varied from 0.08 (SuperPred) to 1.00 (PASS Online) for the initial indications and from 0.00 (SuperPred) to 1.00 (PASS Online) for the repurposed indications. Thus, this analysis demonstrated that the performance of machine learning methods surpassed those of chemical similarity assessments, particularly in the case of novel repurposed indications. ER -