PASS Targets: Ligand-based multi-target computational system based on a public data and naïve Bayes approach

^$Presented at the 8^th International Symposium on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources, CMTPI-2015, June 21–25, 2015, Chios, Greece.

Abstract

Estimation of interactions between drug-like compounds and drug targets is very important for drug discovery and toxicity assessment. Using data extracted from the 19th version of the ChEMBL database (https://www.ebi.ac.uk/chembl) as a training set and a Bayesian-like method realized in PASS software (http://www.way2drug.com/PASSOnline), we developed a computational tool for the prediction of interactions between protein targets and drug-like compounds. After training, PASS Targets became able to predict interactions of drug-like compounds with 2507 protein targets from different organisms based on analysis of structure–activity relationships for 589,107 different chemical compounds. The prediction accuracy, estimated as AUC ROC calculated by the leave-one-out cross-validation and 20-fold cross-validation procedures, was about 96%. Average AUC ROC value was about 90% for the external test set from approximately 700 known drugs interacting with 206 protein targets.

Keywords:

• (Q)SAR
• PASS
• ChEMBL
• in silico drug discovery
• protein targets

Acknowledgements

The work is done in the framework of the Russian State Academies of Sciences Fundamental Research Program for 2013-2020.

Disclosure statement

No financial interest or benefit has been arising from the direct applications of our research.

Notes

^$ Presented at the 8^th International Symposium on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources, CMTPI-2015, June 21–25, 2015, Chios, Greece.