Predicting Drug-target Interaction via Wide and Deep Learning
Yingyi Du, Jihong Wang, Xiaodan Wang, Jiyun Chen, Huiyou Chang · 2018
Identifying the interactions of approval drugs and targets is essential in medicine field, which can facilitate the discovery and reposition of drugs. Due to the tendency towards machine learning, a growing number of computational methods have been applied to the prediction of the drug-target interactions (DTIs). In this paper, we propose a wide and deep learning framework combining a generalized linear model and a deep feed-forward neural network to address the challenge of predicting the DTIs precisely. The proposed method is a joint training of the wide and deep models, which is implemented by feeding the weighted sum of the results obtained from the wide and deep models into a logistic loss function using mini-batch stochastic gradient descent. The results of this experiment indicate that the proposed method increases the accuracy of prediction for DTIs, which is superior to other methods.