IMCHGAN: Inductive Matrix Completion with Heterogeneous Graph Attention Networks for Drug-Target Interactions Prediction.

Computational approaches for prediction of drug-target interactions (DTIs) are highly desired in comparison to traditional biological experiments as its fast and low price. We present a novel Inductive Matrix Completion with Heterogeneous Graph Attention Network approach (IMCHGAN) for predicting DTIs. IMCHGAN first adopts a two-level neural attention mechanism approach to learn drug and target latent feature representations from the DTI heterogeneous network respectively. Then, the learned latent features are fed into the Inductive Matrix Completion (IMC) prediction score model which computes the best projection from drug space onto target space and output DTI score via the inner product of projected drug and target feature representations. IMCHGAN is an end-to-end neural network learning framework where the parameters of both the prediction score model and the feature representation learning model are simultaneously optimized via backpropagation under supervising of the observed known drug-target interactions data. We compare IMCHGAN with other state-of-the-art baselines on two real DTI experimental datasets. The results show that our method is superior to existing methods in terms of AUC and AUPR. Moreover, IMCHGAN also shows it has strong predictive power for novel (unknown) DTIs.