SAG-DTA: Prediction of Drug-Target Affinity Using Self-Attention Graph Network

The prediction of drug-target affinity (DTA) is a crucial step for drug screening and discovery. In this study, a new graph-based prediction model named SAG-DTA (self-attention graph drug-target affinity) was implemented. Unlike previous graph-based methods, the proposed model utilized self-attention mechanisms on the drug molecular graph to obtain effective representations of drugs for DTA prediction. Features of each atom node in the molecular graph were weighted using an attention score before being aggregated as molecule representation. Various self-attention scoring methods were compared in this study. In addition, two pooing architectures, namely, global and hierarchical architectures, were presented and evaluated on benchmark datasets. Results of comparative experiments on both regression and binary classification tasks showed that SAG-DTA was superior to previous sequence-based or other graph-based methods and exhibited good generalization ability.

Automated summary

The study introduced a new graph-based drug-target affinity prediction model named SAG-DTA, which utilized self-attention mechanisms on drug molecular graphs to obtain effective representations. Different self-attention scoring methods were compared, and two pooling architectures were evaluated. Results demonstrated that SAG-DTA outperformed previous methods and exhibited good generalization ability.