Edge-Gated Graph Neural Network for Predicting Protein-Ligand Binding Affinities

Predicting Protein-ligand binding affinities using Deep Learning can significantly shorten the drug development cycle. Recently, Graph neural network models have been developed, and are successfully used to accelerate the development of potential drugs. One major limitation of these GNN models is that they focus on node features (i.e., atom features), as these nodes carry the most important information of molecules. However, atoms are connected via different bonds in molecules, and we argue that such chemical bonds carry critical information for assessing how atomic features should be aggregated. To overcome the lack of bond-related information in earlier models, we here proposed a novel edge-gated graph neural network (egGNN) that predict the binding affinities between proteins and ligands. Specifically, our model treats chemical bonds as gates that control how information is extracted between atoms, this modification enables our model to extract more accurate information for different bonds. We tested our model using the CASF-2016 dataset, and found that the Pearson’s correlation coefficient (R) of egGNN is three percent higher than that of the best tested method (i.e., 0.86 vs 0.83), and the Root Mean Square Error (RMSE) is significantly lower than that of the best tested method (i.e., 1.12 vs 1.23) when compared to state-of-the-art-approaches. In ablation experiments, we demonstrate that our edge-gated feature extraction (EGFE) consoderably improves the performance of GNNs. These results indicate that egGNN represents a promising tool applicable for virtual screening, and should greatly assist in accelerating drug development.