PLAGCA: Predicting protein–ligand binding affinity with the graph cross-attention mechanism

Abstract

Accurate prediction of protein–ligand binding affinity plays a crucial role in drug discovery. However, determining the binding affinity of protein–ligands through biological experimental approaches is both time-consuming and expensive. Although some computational methods have been developed to predict protein–ligands binding affinity, most existing methods extract the global features of proteins and ligands through separate encoders, without considering to extract the local pocket interaction features of protein–ligand complexes, resulting in the limited prediction accuracy. In this work, we proposed a novel Protein–Ligand binding Affinity prediction method (named PLAGCA) by introducing Graph Cross-Attention mechanism to learn the local three-dimensional (3D) features of protein–ligand pockets, and integrating the global sequence/string features and local graph interaction features of protein–ligand complexes. PLAGCA uses sequence encoding and self-attention to extract the protein/ligand global features from protein FASTA sequences/ligand SMILES strings, adopts graph neural network and cross-attention to extract the protein–ligand local interaction features from the molecular structures of protein binding pockets and ligands. All these features are concatenated and input into a multi-layer perceptron (MLP) for predicting the protein–ligand binding affinity. The experimental results show that our PLAGCA outperforms other state-of-the-art computational methods, and it can effectively predict protein–ligand binding affinity with superior generalization capability. PLAGCA can capture the critical functional residues that are important contribution to the protein–ligand binding.