Deep3D-DTA: A Tri-Modal Deep Learning Framework for Binding Affinity Prediction Leveraging 3D Structural Representations of Drugs and Targets

Predicting drug-target affinity (DTA) plays a pivotal role in drug discovery and repurposing. While existing computational approaches predominantly rely on 1D sequences or 2D structural data, they often fail to fully capture the intricate nature of molecular interactions. To address this limitation, we propose Deep3D-DTA, a novel tri-modal deep learning framework that integrates 1D sequence semantics, 2D graph topology, and 3D spatial geometry complementary representations for both drugs and target proteins. The proposed architecture offers three key advancements: First, it employs a pre-trained protein language model to encode amino acid sequences, effectively capturing long-range sequential dependencies. Second, it constructs precise 3D structural representations by computing interatomic distances and bond angles, enabling accurate modeling of the spatial conformations of both proteins and compounds. Third, it leverages a hybrid feature extraction module that combines graph neural networks with multi-head attention mechanisms to learn hierarchical structural patterns. Extensive experiments on three widely used benchmark datasets (Davis, KIBA, and Metz) demonstrate that Deep3D-DTA significantly outperforms state-of-the-art methods in DTA prediction. These results highlight its potential as a robust and reliable computational tool for accelerating drug discovery and reducing development costs through more accurate affinity prediction.