DTA-GTOmega: Enhancing Drug-Target Binding Affinity Prediction with Graph Transformers Using OmegaFold Protein Structures

Understanding drug-protein interactions is crucial for elucidating drug mechanisms and optimizing drug development. However, existing methods have limitations in representing the three-dimensional structure of targets and capturing the complex relationships between drugs and targets. This study proposes a new method, DTA-GTOmega, for predicting drug-target binding affinity. DTA-GTOmega utilizes OmegaFold to predict protein three-dimensional structure and construct target graphs, while processing drug SMILES sequences with RDKit to generate drug graphs. By employing multi-layer graph transformer modules and co-attention modules, this method effectively integrates atomic-level features of drugs and residue-level features of targets, accurately modeling the complex interactions between drugs and targets, thereby significantly improving the accuracy of binding affinity predictions. Our method outperforms existing techniques on benchmark datasets such as KIBA, Davis, and BindingDB_Kd under cold-start setting. Moreover, DTA-GTOmega demonstrates competitive performance in real-world DTI scenarios involving DrugBank data and drug-target interactions related to cardiovascular and nervous system-related diseases, highlighting its robust generalization capabilities. Additionally, the introduced DTI evaluation metrics further validate DTA-GTOmega's potential in handling imbalanced data.