title: Classification of Human Pregnane X Receptor (hPXR) Activators and Non-Activators by Machine Learning Techniques: A Multifaceted Approach.
abstract: The Human Pregnane X Receptor (hPXR) is a regulator of drug metabolising enzymes (DME) and efflux transporters (ET). The prediction of hPXR activators and non-activators has pharmaceutical importance to predict the multiple drug resistance (MDR) and drug-drug interactions (DDI). In this study, we developed and validated the computational prediction models to classify hPXR activators and non-activators. We employed four machine learning methods support vector machine (SVM), k-nearest neighbour (k-NN), random forest (RF) and naïve bayesian (NB). These methods were used to develop molecular and fingerprint based descriptors for the prediction of hPXR activators and non-activators. Total 529 molecules consitsting of 317 activators and 212 non-activators were used for model development. The overall prediction accuracy of models was 69% to 99% to classify hPXR activators and nonactivators using RDkit descriptors. In case of 5 and 10-fold cross validation the prediction accuracy for training set is 74% to 82% and 79% to 83% for hPXR activators respectively and 50% to 62% and 49% to 65% non-activators, respectively. The external test prediction is between 59% to 73% for hPXR activators and 55% to 68% for hPXR non-activators. In addition, consensus models were developed in which the best model shows overall 75% to 83% accuracy for fingerprint and RDkit descriptors, respectively. The best developed model will be utilized for the prediction of hPXR activators and non-activators.
authors.forename: Vijay
authors.initials: V
authors.lastname: Rathod
authors.forename: Vilas
authors.initials: V
authors.lastname: Belekar
authors.forename: Prabha
authors.initials: P
authors.lastname: Garg
authors.affiliation: Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research, S.A.S Nagar, Punjab-160062, India. abhays@niper.ac.in.
authors.forename: Abhay T
authors.initials: AT
authors.lastname: Sangamwar
mesh_terms: D001499:Bayes Theorem; D004347:Drug Interactions; D018432:Drug Resistance, Multiple; D006801:Humans; D000069550:Machine Learning; D000077297:Pregnane X Receptor; D011278:Pregnanes / Q000378:metabolism; D011987:Receptors, Steroid / Q000378:metabolism*; D060388:Support Vector Machine
chemical_list: D000077297:Pregnane X Receptor; D011278:Pregnanes; D011987:Receptors, Steroid