Journal Article

CNN-DDI: a learning-based method for predicting drug–drug interactions using convolution neural networks

Zang, Tianyi, Zhang, Chengcheng, Lu, Yao

2022

Overview

Background Drug–drug interactions (DDIs) are the reactions between drugs. They are compartmentalized into three types: synergistic, antagonistic and no reaction. As a rapidly developing technology, predicting DDIs-associated events is getting more and more attention and application in drug development and disease diagnosis fields. In this work, we study not only whether the two drugs interact, but also specific interaction types. And we propose a learning-based method using convolution neural networks to learn feature representations and predict DDIs. Results In this paper, we proposed a novel algorithm using a CNN architecture, named CNN-DDI, to predict drug–drug interactions. First, we extract feature interactions from drug categories, targets, pathways and enzymes as feature vectors and employ the Jaccard similarity as the measurement of drugs similarity. Then, based on the representation of features, we build a new convolution neural network as the DDIs’ predictor. Conclusion The experimental results indicate that drug categories is effective as a new feature type applied to CNN-DDI method. And using multiple features is more informative and more effective than single feature. It can be concluded that CNN-DDI has more superiority than other existing algorithms on task of predicting DDIs.

• Publisher: BioMed Central,BioMed Central Ltd,BMC
• Subject: Algorithms / Bioinformatics / Biomedical and Life Sciences / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Convolutional neural network / Drug categories / Drug Development / Drug Interactions / Drug–drug interactions / Life Sciences / Machine learning / Methodology / Microarrays / Multiple features combination / Neural networks / Neural Networks, Computer / Pharmacology, Experimental / Research Design