{"ID":2870936,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.13476","arxiv_id":"2509.13476","title":"A Geometric Graph-Based Deep Learning Model for Drug-Target Affinity Prediction","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.","short_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 t...","url_abs":"https://arxiv.org/abs/2509.13476","url_pdf":"https://arxiv.org/pdf/2509.13476v1","authors":"[\"Md Masud Rana\",\"Farjana Tasnim Mukta\",\"Duc D. Nguyen\"]","published":"2025-09-15T14:06:39Z","proceeding":"q-bio.BM","tasks":"[\"q-bio.BM\",\"cs.LG\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}