A Predictive Model for Toxicity Effects Assessment of Biotransformed Hepatic Drugs Using Iterative Sampling Method

Abstract

Measuring toxicity is one of the main steps in drug development. Hence, there is a high demand for computational models to predict the toxicity effects of the potential drugs. In this study, we used a dataset, which consists of four toxicity effects:mutagenic, tumorigenic, irritant and reproductive effects. The proposed model consists of three phases. In the first phase, rough set-based methods are used to select the most discriminative features for reducing the classification time and improving the classification performance. Due to the imbalanced class distribution, in the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling and Synthetic Minority Oversampling Technique are used to solve the problem of imbalanced datasets. ITerative Sampling (ITS) method is proposed to avoid the limitations of those methods. ITS method has two steps. The first step (sampling step) iteratively modifies the prior distribution of the minority and majority classes. In the second step, a data cleaning method is used to remove the overlapping that is produced from the first step. In the third phase, Bagging classifier is used to classify an unknown drug into toxic or non-toxic. The experimental results proved that the proposed model performed well in classifying the unknown samples according to all toxic effects in the imbalanced datasets.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

“…In addition, both boosting algorithms generated a strong toxicity predictor from an ensemble of weak predictors. Another ensemble technique was reported by Tharwat et al 746 in 2016. In this case, four different toxic effects (risk factors) of approved drugs were assessed with a Bagging classifier.…”

Reporting the Anti-neuroinflammatory Potential of Selected Spondias mombin Flavonoids through Network Pharmacology and Molecular Dynamics Simulations

“…This includes assessments of their potential mutagenic, tumorigenic, reproductive effects, and irritant properties. This information is vital for understanding the safety pro le of these compounds (Tharwat et al, 2016).…”

On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach

“…Addition of physico-chemical properties selected by genetic algorithm to biGRU featurization provided 0.195 of improvement in AUC-ROC to toxicity prediction tasks. Tharwat et al [206] used 31 molecular descriptors in prediction of four toxicity tasks in combination with multiple data sampling strategies to build an ensemble learning framework. In this way, they achieved the best performance on the four different toxicity tasks by an entropy-based feature selection method [207] .…”