GSDeep-DTA: A Hybrid Graph and Sequence-based Deep Learning Framework for Robust Drug-Target Affinity Prediction

Robust prediction of drug-target binding affinity (DTA) is essential for accelerating drug discovery pipelines, as it reduces experimental failures in cold-start scenarios where novel compounds or targets are involved. Although existing DTA models primarily rely on sequence-based or graph-based representations, a limited number of studies have explored the integration of both approaches. However, effectively encoding and integrating the diverse features of drugs and proteins while enhancing predictive performance remains a challenging task. This work proposes a hybrid graph- and sequence-based framework for robust DTA prediction, GSDeep-DTA. The model integrates graph neural networks to represent drug molecular structures and protein contact maps, and cascades Convolutional Neural Network - Bidirectional Long Short-Term Memory modules with transformer embeddings to hierarchically capture sequential dependencies. We adopt a weighted sum fusion mechanism to integrate these heterogeneous features, which balances effectiveness and simplicity compared to more complex fusion techniques. Experimental results on the Davis benchmark dataset show that our model outperforms state-of-the-art DTA prediction models. In addition, we evaluate its ability to generalize in cold-start scenarios, assessing its performance on novel drugs and/or proteins. Our findings highlight the potential of hybrid graph-and sequence-based deep learning models for improved DTA prediction.