SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features

Abstract

Abstract Background Drug–target affinity (DTA) prediction is a critical step in the field of drug discovery. In recent years, deep learning-based methods have emerged for DTA prediction. In order to solve the problem of fusion of substructure information of drug molecular graphs and utilize multi-scale information of protein, a self-supervised pre-training model based on substructure extraction and multi-scale features is proposed in this paper. Results For drug molecules, the model obtains substructure information through the method of probability matrix, and the contrastive learning method is implemented on the graph-level representation and subgraph-level representation to pre-train the graph encoder for downstream tasks. For targets, a BiLSTM method that integrates multi-scale features is used to capture long-distance relationships in the amino acid sequence. The experimental results showed that our model achieved better performance for DTA prediction. Conclusions The proposed model improves the performance of the DTA prediction, which provides a novel strategy based on substructure extraction and multi-scale features.

Keywords

• Drug–target binding affinity
• Self-supervised learning
• Mutual information
• Multi-scale features