Inacc - 3l: Identification Of Anticancer Compounds From Herbal Database And Evaluation Of Their Therapeutic Efficacy Using Ensemble Learning Models

Abstract

On a positive aspect, machine learning approaches which have been intensively grown in recent years, are considered to be robust and effective solutions to address various biological and chemical issues, including drug discovery because it can save time, cost and human efforts. This study aims to develop an ensemble learning model to establish a relationship between chemical structures of natural compounds and their anti-cancerous inhibition activities. Consequently, three commonly used models, including Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB), were selected to deal with two classification problems and one regression problem. The used compound data were collected from naturally occurring Plant-based Anti-cancerous Compound-Activity-Target (NPACT) and Anticancer Herbs database of Systems Pharmacology (CancerHSP) databases. Each molecule was expressed using 12 types of molecular fingerprints as inputs for modeling to identify anticancer compounds as well as evaluating their therapeutic efficacies. The constructed well-fitted models can assist experimental scientists to screen for potent natural compounds with strong anticancer activity. The results show that three models efficiently classify inhibitors and non-inhibitors with an accuracy of up to 0.7. The experiments also reveal that good outcomes were not obtained for regression tasks with a low coefficient of determination (R2<0.5) and high mean squared error (MSE>11). Furthermore, this study suggests that the XGB model is the bestperformed model for almost evaluation metrics in both classification and regression problems.