Toxicity prediction of small drug molecules of androgen receptor using multilevel ensemble model

Abstract

In this study, efforts are created to develop a quantitative structure–activity relationship (QSAR)-based model, which are used for the prediction of toxicities to reduce testing in animals, time, and money in the early stages of drug development. An efficient machine learning model is developed to predict the toxicity of those drug molecules which binds to the androgen receptor (AR). Toxicity prediction is performed in terms of their activity, activity score, potency, and efficacy by using various physicochemical properties. A multilevel ensemble model is proposed, where its first level is performed ensemble-based classification of activity, and the second level is performed ensemble-based regression of activity score, potency, and efficacy of only those drug molecules which have been found active during the classification level. The AR dataset has 10,273 drug molecules where 461 are active, and 9812 are inactive, and each drug molecule has 1444 features. Therefore, our dataset is highly imbalanced having a very large number of features. Initially, we performed feature selection then the class imbalance problem is resolved. The [Formula: see text]-fold cross-validation is accomplished to measure the consistency of the model. Finally, our proposed multilevel ensemble model has been validated and compared with some existing models.

Comparison of Machine Learning Models for the Androgen Receptor

“…Grisoni et al 25 not only presented the advantages of each algorithm but also evaluated the structural features for AR binding, and Manganelli et al 19 evaluated misclassified binding chemicals. Gupta and Rana 26 developed a multilevel ensemble model, which first applied a random forest model to classify compounds and then applied multiple activity scores using four methods (linear, decision trees, random forest, neural network) to a Tox21 AR agonism training data set. Idakwo et al 27 utilized both agonist and antagonist data sets from Tox21 for random forest and deep learning and investigated the chemical similarity between prediction classes to further analyze accuracy.…”

Prediction of COVID-19 confirmed, death, and cured cases in India using random forest model

“…The accuracy is computed as the percentage deviation of the predicted target concerning the actual target with some acceptable error. It is the main performance evaluation parameter of any machine learning model [7,14] .…”

Toxicity prediction based on artificial intelligence: A multidisciplinary overview

“…On the other hand, alterations in the normal functioning of estrogen and androgen receptors (ER and AR) may cause endocrine disruption and lead to adverse effects on health such as tissue or organ proliferation, reproductive disorders, metabolic disorders, or even cancers. Gupta and Rana 106 developed an efficient ML model with the potential to be applied to predict the toxicity of those drug molecules which bind to the androgen receptor, based on a multilevel ensemble model that first performed a classification of activity and a second performed regression of activity score, potency, and efficacy of only those drug molecules which were found to be active during the classification level. Distribution of data between the training and testing sets was 70% and 30%, respectively.…”