Encoding Protein-Ligand Interactions: Binding Affinity Prediction with Multigraph-based Modeling and Graph Convolutional Network

Nedra Mekni, Hosein Fooladi, Ugo Perricone , and Thierry Langer

Abstract: Machine learning models are employed to enhance the speed and provide novel insights in drug discovery due to their demonstrated effectiveness in predicting properties of small molecules like pKa, solubility, and binding affinity. These approaches accelerate drug discovery by helping researchers efficiently identify, prioritize, and optimize compounds. Nonetheless, when investigating properties that depend on the interaction between a ligand and its corresponding protein, a compelling need arises to incorporate the protein counterpart information within the models. Recently, graph neural networks (GNNs) have been developed to incorporate 3D structural information to improve our understanding of the underlying protein-ligand interactions. However, incorporating 3D information into GNNs is not always straightforward.

To address the challenge, we introduce a model called InterGraph, which models the protein-ligand interaction as topological multigraphs. By leveraging a topological representation, InterGraph offers a comprehensive approach to a graph representation of the intricate spatial organization and connectivity patterns within protein-ligand systems. We introduce interaction spheres that assign varying edge densities, capturing the proximity-based influence of interactions. This approach enables us to capture the characteristics of the interaction network, filtering out the ones that are beyond 9 Å from the ligand since they are not considered relevant or established. Finally, we trained the model using a ligand binding dataset from PDBbind and tested it on a hold-out test set, achieving an RMSE value of 1.34. Our findings have demonstrated the power of the multigraph to encode the importance of close interactions, a factor that is relevant in the context of binding affinity. On average, our model accurately predicts binding affinity values for several protein-ligand complexes and exhibits higher accuracy for hydrolase, lyase, and families of proteins involved in mediating protein-protein interactions. Additionally, the Intergraph method displayed sensitivity to the binding mode when compared to a set of complexes that had undergone redocking calculations.

@article{mekni2023encoding,
  title={Encoding Protein-Ligand Interactions: Binding Affinity Prediction with Multigraph-based Modeling and Graph Convolutional Network},
  author={Mekni, Nedra and Fooladi, Hosein and Perricone, Ugo and Langer, Thierry},
  year={2023}
}