Unbalance Quantitative Structure Activity Relationship Problem Reduction in Drug Design

Abstract

Problem statement: Activities of drug molecules can be predicted by Quantitative Structure Activity Relationship (QSAR) models, which overcome the disadvantage of high cost and long cycle by employing traditional experimental methods. With the fact that number of drug molecules with positive activity is rather fewer than that with negatives, it is important to predict molecular activities considering such an unbalanced situation. Approach: Asymmetric bagging and feature selection was introduced into the problem and Asymmetric Bagging of Support Vector Machines (AB-SVM) was proposed on predicting drug activities to treat unbalanced problem. At the same time, features extracted from structures of drug molecules affected prediction accuracy of QSAR models. Hybrid algorithm named SPRAG was proposed, which applied an embedded feature selection method to remove redundant and irrelevant features for AB-SVM. Results: Numerical experimental results on a data set of molecular activities showed that AB-SVM improved AUC and sensitivity values of molecular activities and SPRAG with feature selection further helps to improve prediction ability. Conclusion: Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which could be furthermore improved by performing feature selection to select relevant features from the drug.