How good are publicly available web services that predict bioactivity profiles for drug repurposing?^$

$ Presented at the 9th International Symposium on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources, CMTPI-2017, 27–30 October 2017, Goa, India.

Abstract

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.

Keywords:

• Drug repurposing
• bioactivity profile prediction
• similarity assessment
• machine learning
• web services
• performance evaluation

Acknowledgements

The work is supported by the RSF-DST grant No. 16-45-02012-INT/RUS/RSF/12.

Notes

$ Presented at the 9th International Symposium on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources, CMTPI-2017, 27–30 October 2017, Goa, India.