A Geometric Graph-Based Deep Learning Model for Drug-Target Affinity Prediction

Md Masud Rana; Farjana Tasnim Mukta; Duc D. Nguyen

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arXiv · 2509.13476

A Geometric Graph-Based Deep Learning Model for Drug-Target Affinity Prediction

Md Masud Rana·Farjana Tasnim Mukta·Duc D. Nguyen

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Abstract

In structure-based drug design, accurately estimating the binding affinity between a candidate ligand and its protein receptor is a central challenge. Recent advances in artificial intelligence, particularly deep learning, have demonstrated superior performance over traditional empirical and physics-based methods for this task, enabled by the growing availability of structural and experimental affinity data. In this work, we introduce DeepGGL, a deep convolutional neural network that integrates residual connections and an attention mechanism within a geometric graph learning framework. By leveraging multiscale weighted colored bipartite subgraphs, DeepGGL effectively captures fine-grained atom-level interactions in protein-ligand complexes across multiple scales. We benchmarked DeepGGL against established models on CASF-2013 and CASF-2016, where it achieved state-of-the-art performance with significant improvements across diverse evaluation metrics. To further assess robustness and generalization, we tested the model on the CSAR-NRC-HiQ dataset and the PDBbind v2019 holdout set. DeepGGL consistently maintained high predictive accuracy, highlighting its adaptability and reliability for binding affinity prediction in structure-based drug discovery.

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BibTeX RIS

Md Masud Rana, Farjana Tasnim Mukta, Duc D. Nguyen. 2025-09-15. A Geometric Graph-Based Deep Learning Model for Drug-Target Affinity Prediction. https://arxiv.org/abs/2509.13476

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