Leveraging scaffold information to predict protein-ligand binding affinity with an empirical graph neural network

Protein-ligand binding affinity prediction is an important task in structural bioinformatics for drug discovery and design. Although various scoring functions (SFs) have been proposed, it remains challenging to accurately evaluate the binding affinity of a protein-ligand complex with the known bound structure because of the potential preference of scoring system. In recent years, deep learning (DL) techniques have been applied to SFs without sophisticated feature engineering. Nevertheless, existing methods cannot model the differential contribution of atoms in various regions of proteins, and the relationship between atom properties and intermolecular distance is also not fully explored. We propose a novel empirical graph neural network for accurate protein-ligand binding affinity prediction (EGNA). Graphs of protein, ligand and their interactions are constructed based on different regions of each bound complex. Proteins and ligands are effectively represented by graph convolutional layers, enabling the EGNA to capture interaction patterns precisely by simulating empirical SFs. The contributions of different factors on binding affinity can thus be transparently investigated. EGNA is compared with the state-of-the-art machine learning-based SFs on two widely used benchmark data sets. The results demonstrate the superiority of EGNA and its good generalization capability.

Automated summary

Protein-ligand binding affinity prediction is a challenging task in structural bioinformatics for drug discovery and design. Existing methods using scoring functions (SFs) for evaluation may have potential biases. A novel empirical graph neural network (EGNA) is proposed to accurately predict binding affinity by effectively representing proteins and ligands using graph convolutional layers and capturing interaction patterns. EGNA outperforms state-of-the-art machine learning-based SFs, demonstrating its superiority and generalization capability.