PLMAM-PLA: A Method Using Pretrained Language Models and Attention Mechanisms for Protein-Ligand Binding Affinity Prediction

Protein-ligand binding affinity measures the strength of interactions between proteins and ligands. Accurately predicting this value is crucial for drug discovery and estimating enzyme kinetic parameters. In recent years, various computational models based on deep learning algorithms have been developed for predicting protein-ligand binding affinity. Most of these require data on protein structure or pockets in addition to protein sequences and ligand SMILES strings. Although integrating structural or pocket information can enhance prediction performances, sequence-based affinity prediction methods using only protein sequences and ligand SMILES strings are more convenient and efficient in practice. We have developed a novel sequence-based deep learning model, called PLMAM-PLA, to predict protein-ligand binding affinity. This model simultaneously extracts global and local features from both protein sequences and ligand SMILES by leveraging pretrained language models (ESM-2 and MolFormer) and dilated convolutional neural networks. The features are enhanced by SKNets and SENets and are further fused by successively using cross-attention and self-attention mechanisms. The output module provides the final affinity prediction value. Ablation studies emphasize the important contributions of the different modules, while visualization experiments demonstrate the efficacy of PLMAM-PLA in capturing meaningful feature representations. Additionally, case studies highlight the powerful generalization capabilities of the model, while comparisons with state-of-the-art models confirm its superior performance in predicting protein-ligand binding affinities.