GSATDTA , a triple channel model that takes sequential and structural information to generate DTA predictions; a Graph Neural Network (GNN) learns drugs' topological features followed by a graph-sequence attention layer to catch important structural and sequential features, while the protein target is studied by a separate transformer, and at the end both outcomes are concatenated and go through a large number of NNs ending with a regression layer that gives a predicted DTA value. GSATDTA outperformed tested benchmarks on two different datasets.