Editor's Perspective: Molecular Descriptor Landscape in the Twenty First Century and its Proper Use for Computer-Aided Drug Design

Prediction of K562 Cells Functional Inhibitors Based on Machine Learning Approaches

BACKGROUND

β thalassemia is a common monogenic genetic disease that is very harmful to human health. The disease arises is due to the deletion of or defects in β-globin, which reduces synthesis of the β-globin chain, resulting in a relatively excess number of α-chains. The formation of inclusion bodies deposited on the cell membrane causes a decrease in the ability of red blood cells to deform and a group of hereditary haemolytic diseases caused by massive destruction in the spleen.

METHODS

In this work, machine learning algorithms were employed to build a prediction model for inhibitors against K562 based on 117 inhibitors and 190 non-inhibitors.

RESULTS

The overall accuracy (ACC) of a 10-fold cross-validation test and an independent set test using Adaboost were 83.1% and 78.0%, respectively, surpassing Bayes Net, Random Forest, Random Tree, C4.5, SVM, KNN and Bagging.

CONCLUSION

This study indicated that Adaboost could be applied to build a learning model in the prediction of inhibitors against K526 cells.