Improving structure-based protein-ligand affinity prediction by graph representation learning and ensemble learning.

ABSTRACT: Predicting protein-ligand binding affinity presents a viable solution for accelerating the discovery of new lead compounds. The recent widespread application of machine learning approaches, especially graph neural networks, has brought new advancements in this field. However, some existing structure-based methods treat protein macromolecules and ligand small molecules in the same way and ignore the data heterogeneity, potentially leading to incomplete exploration of the biochemical information of ligands. In this work, we propose LGN, a graph neural network-based fusion model with extra ligand feature extraction to effectively capture local features and global features within the protein-ligand complex, and make use of interaction fingerprints. By combining the ligand-based features and in

SUBMITTER: Guo J 

PROVIDER: S-EPMC10793902 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature