Skip to main content
padlock icon - secure page this page is secure

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

Buy Article:

$60.00 + tax (Refund Policy)

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 ( as a training set and a Bayesian-like method realized in PASS software (, 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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: (Q)SAR; ChEMBL; PASS; in silico drug discovery; protein targets

Document Type: Research Article

Affiliations: Department for Bioinformatics; Institute of Biomedical Chemistry, Pirogov Russian National Research Medical University, Moscow, Russia

Publication date: October 3, 2015

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more