Background: Identifying Drug-Target Interactions (DTIs) is a major challenge for
current drug discovery and drug repositioning. Compared to traditional experimental approaches,
in silico methods are fast and inexpensive. With the increase in open-access experimental data,
numerous computational methods have been applied to predict DTIs.
Methods: In this study, we propose an end-to-end learning model of Factorization Machine and
Deep Neural Network (FM-DNN), which emphasizes both low-order (first or second order) and
high-order (higher than second order) feature interactions without any feature engineering other
than raw features. This approach combines the power of FM and DNN learning for feature
learning in a new neural network architecture.
Results: The experimental DTI basic features include drug characteristics (609), target
characteristics (1819), plus drug ID, target ID, total 2430. We compare 8 models such as SVM,
GBDT, WIDE-DEEP etc, the FM-DNN algorithm model obtains the best results of AUC(0.8866)
Conclusion: Feature engineering is a job that requires expert knowledge, it is often difficult and
time-consuming to achieve good results. FM-DNN can auto learn a lower-order expression by FM
and a high-order expression by DNN.FM-DNN model has outstanding advantages over other
commonly used models.