1. Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks
- Author
-
Yi Cui, Z.F. Zhang, Wen Zhang, Yanhong Deng, Shichao Liu, Y. Zhang, and Yunping Qiu
- Subjects
Artificial neural network ,Drug discovery ,Computer science ,business.industry ,Graph embedding ,Applied Mathematics ,Drug-drug interaction ,Construct (python library) ,Machine learning ,computer.software_genre ,Multiple data ,Feature (machine learning) ,Key (cryptography) ,Genetics ,Artificial intelligence ,business ,computer ,Biotechnology - Abstract
Drug-drug interactions are one of the main concerns in drug discovery. Accurate prediction of drug-drug interactions plays a key role in increasing the efficiency of drug research and safety when multiple drugs are c o-prescribed. With various data sources that describe the relationships and properties between drugs, the comprehensive approach that integrates multiple data sources would be considerably effective in making high-accuracy prediction. In this paper, we propose a Deep Attention Neural Network based Drug-Drug Interaction prediction framework, abbreviated as DANN-DDI, to predict unobserved drug-drug interactions. First, we construct multiple drug feature networks and learn drug representations from these networks using the graph embedding method; then, we concatenate the learned drug embeddings and design an attention neural network to learn representations of drug-drug pairs; finally, we adopt a deep neural network to accurately predict drug-drug interactions. The experimental results demonstrate that our model DANN-DDI has improved prediction performance compared with state-of-the-art methods. Moreover, the proposed model can predict novel drug-drug interactions and drug-drug interaction-associated events.
- Published
- 2023