GraphscoreDTA: optimized graph neural network for protein-ligand binding affinity prediction.

ABSTRACT:

Motivation

Computational approaches for identifying the protein-ligand binding affinity can greatly facilitate drug discovery and development. At present, many deep learning-based models are proposed to predict the protein-ligand binding affinity and achieve significant performance improvement. However, protein-ligand binding affinity prediction still has fundamental challenges. One challenge is that the mutual information between proteins and ligands is hard to capture. Another challenge is how to find and highlight the important atoms of the ligands and residues of the proteins.

Results

To solve these limitations, we develop a novel graph neural network strategy with the Vina distance optimization terms (GraphscoreDTA) for predicting protein-ligand binding affinity, which

SUBMITTER: Wang K 

PROVIDER: S-EPMC10243863 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature