MSIGN: A deep learning framework based on multi-scale interaction graph neural networks for predicting binding of synthetic cannabinoids to receptors

Abstract

Deep learning-based models have been extensively applied to the task of protein-ligand binding affinity (PLA) prediction. Current 3D ligand-complex-based GNNs, though advanced, still struggle with accuracy and generalization due to their overreliance on atomic-level physical features and neglect of chemical space dynamics, leading to data memorization rather than robust learning. To address these issues, we propose a deep learning model based on a Multi-Scale Interaction Graph Neural Network (MSIGN). By constructing ligand functional group graphs and protein amino acid graphs, we introduce chemical information features into the model, which are combined with physical features to enhance binding affinity prediction. Especially, we innovatively adopt a pre-training and fine-tuning training approach in the PLA domain to improve the model's generalization capability on downstream tasks (this study focuses on the binding affinity prediction of synthetic cannabinoids), and we validated the MSIGN model predictions with wet experiments such as SPR on three novel synthetic cannabinoids. Furthermore, we analyze the impact of different fine-tuning strategies on the model's generalization ability. Multiple results collectively demonstrate the superiority of our MSIGN model design, providing a novel approach for future PLA prediction.