Open AccessDevelopment of cardiotoxicity model using ligand-centric and receptor-centric descriptors
Author(s) -
Chirag Patel,
Sivakumar Prasanth Kumar,
Rakesh Rawal,
Manishkumar B Thaker,
Himanshu Pandya
Publication year - 2020
Publication title -
toxicology research and application
Language(s) - English
Resource type - Journals
ISSN - 2397-8473
DOI - 10.1177/2397847320971259
Subject(s) - linear discriminant analysis , discriminant function analysis , cardiotoxicity , artificial intelligence , test set , machine learning , discriminant , receiver operating characteristic , molecular descriptor , computer science , quantitative structure–activity relationship , pattern recognition (psychology) , computational biology , chemistry , biology , organic chemistry , toxicity
Background: Bioinformatics and statistical analysis have been employed to develop a classification model to distinguish toxic and non-toxic molecules.Aims: The primary objective of this study is to enumerate the cut-off values of various physico-chemical (ligand-centric) and target interaction (receptor-centric) descriptors which forms the basis for classifying cardiotoxic and non-toxic molecules. We also sought correlation of molecular docking, absorption, distribution, metabolism, excretion, and toxicology (ADMET) parameters, Lipinski rules, physico-chemical parameters, etc. of human cardiotoxicity drugs.Methods: A training and test set of 91 compounds were applied to linear discriminant analysis (LDA) using 2D and 3D descriptors as discriminating variables representing various molecular modeling parameters to identify which function of descriptor type is responsible for cardiotoxicity. Internal validation was performed using the leave-one-out cross-validation methodology ensuing in good results, assuring the stability of the discriminant function (DF).Results: The values of the statistical parameters Fisher Discriminant Analysis (FDA) and Wilk’s λ for the DF showed reliable statistical significance, as long as the success rate in the prediction for both the training and the test set attained more than 93% accuracy, 87.50% sensitivity and 94.74% specificity.Conclusion: The predictive model was built using a hybrid approach using organ-specific targets for docking and ADMET properties for the FDA (Food and Drug Administration) approved and withdrawn drugs. Classifiers were developed by linear discriminant analysis and the cut-off was enumerated by receiver operating characteristic curve (ROC) analysis to achieve reliable specificity and sensitivity.