Fig. 2

Schematic Representation of the AiGPro Architecture. The diagram illustrates the proposed framework, which includes four modules: (1) Tokenizing and embedding the protein sequence from MSA and compound smiles inputs and data representation. (2) using the Multi-Scale Context-Aggregation module based on dilated convolution to extract multiscale features from both the input protein sequence and compound SMILES. (3) The bidirectional multi-head cross attention (BMCA) for intermolecular features between the protein and ligand (4) Output module to predict unknown interaction in a drug–target pair, which can address classification and regression tasks based on user consideration