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).

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.

背景：识别药物-靶标相互作用（DTI）是当前药物发现和药物重新定位的主要挑战。与传统的实验方法相比，计算机方法是快速且廉价的。随着开放存取实验数据的增加，许多计算方法已应用于预测DTI。

方法：在这项研究中，我们提出了因子分解机器和深度神经网络（FM-DNN）的端到端学习模型，该模型强调低阶（一阶或二阶）和高阶（高于二阶） ）功能交互，除了原始功能外，没有任何其他功能工程。这种方法结合了FM和DNN学习的功能，可以在新的神经网络体系结构中进行特征学习。

结果：实验性DTI的基本特征包括药物特征（609），目标特征（1819），以及药物ID，目标ID，总数2430。我们比较了8种模型，例如SVM，GBDT，WIDE-DEEP等，FM-DNN算法模型获得了AUC（0.8866）和AUPR（0.8281）的最佳结果。

结论：特征工程是一项需要专家知识的工作，要获得良好的结果通常很困难且耗时。FM-DNN可以通过FM自动学习低阶表达式，并可以通过DNN自动学习高阶表达式。FM-DNN模型相对于其他常用模型具有突出的优势。