Jihong Wang, Hao Wang, Xiaodan Wang and Huiyou Chang*
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 AUPR(0.8281).
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.
drug-target interactions, prediction, factorization machines, DNN learning, machine learning, DrugBank
School of Data and Computer Science,Sun Yat-Sen University, No.132 Waihuan East Road, 510000 Guangzhou, School of Data and Computer Science,Sun Yat-Sen University, No.132 Waihuan East Road, 510000 Guangzhou, School of Pharmaceutical Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, No.9-13 Wuguishan Avenue of Life Street, 528458, Zhongshan, School of Data and Computer Science,Sun Yat-Sen University, No.132 Waihuan East Road, 510000 Guangzhou