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.