A Geometric Graph-Based Deep Learning Model for Drug-Target Affinity Prediction
This addresses the challenge of accurate binding affinity prediction for drug discovery researchers, representing a strong incremental advance over existing deep learning methods.
The paper tackled the problem of predicting drug-target binding affinity in structure-based drug design by introducing DeepGGL, a deep convolutional neural network that achieved state-of-the-art performance on CASF-2013 and CASF-2016 benchmarks with significant improvements across diverse evaluation metrics.
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.