MGMA-DTI: Drug target interaction prediction using multi-order gated convolution and multi-attention fusion

Abstract

Accurately predicting drug-target interactions (DTI) is crucial for drug discovery and can reduce drug development costs. Recent deep learning-based DTI predictions have demonstrated promising performance, but they still face two challenges: (i) The over-reliance on the extraction of local features and insufficient learning of global features limit the model’s performance. (ii) The lack of effective fusion of drug-target interaction features leads to the lack of interpretability of the model. To address these challenges, we propose a new model for predicting drug-target interactions based on multi-order gated convolution and multi-attention fusion, MGMA-DTI. The drug feature encoder obtains a two-dimensional molecular graph based on the drug’s SMILES string and uses a graph convolutional neural network to encode the drug features. The protein encoder is based on a multi-order gated convolution, which enhances the model’s ability to capture global feature between amino acid sequences. In order to better achieve interactive learning between drugs and proteins, we designed a multi-attention fusion module that effectively captures the drug-target interaction features. Experimental results show that MGMA-DTI outperforms other baseline models on three benchmark datasets: BindingDB, BioSNAP, and Human. Case studies further demonstrate that the model provides valuable insights for drug discovery. In addition, our model provides molecular-level interpretability, which can provide more scientifically meaningful guidance.