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) and
Conclusions: 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.