Enhanced Protein-Ligand Affinity Prediction with Conditional Updating and Proximity Embedding

The protein-ligand affinity prediction task aims to predict the binding strength of small molecule ligands to specific proteins, which is crucial in the fields of drug design and molecular biology, and can accelerate the drug discovery. The structure complementarity between protein and ligand plays a critical role in determining binding strength , but most of current deep learning-based affinity prediction models usually extracted the features of protein and ligand by these two detached modules. which limits the exchange of information for capturing interactions and struggles to capture proteins’ important residues. To address these limitations. we introduce CIP. which takes the combination of GNN, Conditional Updating and Proximity Embedding for the first time. Compared to existing models, CIP has several significant advantages. First, Conditional updating modifies the ligand’s local features based on the protein’s global features, and vice versa , enhancing structural complementarity to capture intricate interactions. Second, encoding the relative distances between proximal residue-atom pairs highlights critical residues. Additionally, our model integrates covalent and noncovalent interactions to obtain more comprehensive graph representations. Experiments on the PDB-bind 2016 benchmark demonstrate that CIP outperforms the original method with improvements of 2.3%, 3.2%, 2.8%, 3.8%, and 3.2% across five baselines. Furthermore, visualization results reveal that CIP effectively captures intricate interactions and crucial residues.The implemented code and dataset are available online at https://github.com/xfcui/CIP.