Deep cross-modal representation fusion learning for enhanced drug target affinity prediction.

Abstract

Prediction of Drug Target Affinity (DTA) is essential for accelerating computational drug discovery and reducing experimental costs. However, traditional experimental approaches for DTA estimation are resource-intensive and are further challenged by the structural flexibility of both drugs and target proteins. In this work, we propose the PCBERT-GAT-DFFNN-DTA model, a three-stage deep cross-modal representation fusion framework for accurate DTA prediction. In the first stage, variable-length protein sequences are transformed into contextual representations using ProtBERT to obtain fixed-size protein embeddings. Drug molecules are represented using two modalities: sequence-based embeddings generated from ChemBERT and structure-based embeddings learned from molecular graphs using a Graph Attention Network (GAT). In the second stage, each modality is processed through dedicated subnetworks to refine features and reduce dimensionality while preserving modality-specific information. In the final stage, the refined representations are fused and passed to a Deep Feed-Forward Neural Network (DFFNN) to predict drug target binding affinity. The proposed model consistently outperformed most baseline methods under the S1-S3 evaluation settings across the benchmark datasets. Under the more challenging S4 blind setting, the model achieved strong performance on the KIBA dataset and competitive results on the Davis and Metz datasets. Compared with LLMDTA, the proposed approach achieves significant improvements in R2 scores across all datasets, demonstrating its effectiveness in learning complex drug protein interactions for reliable DTA prediction.