GNNSeq: A Sequence-Based Graph Neural Network for Predicting Protein-Ligand Binding Affinity.

ABSTRACT: Background/Objectives: Accurately predicting protein-ligand binding affinity is essential in drug discovery for identifying effective compounds. While existing sequence-based machine learning models for binding affinity prediction have shown potential, they lack accuracy and robustness in pattern recognition, which limits their generalizability across diverse and novel binding complexes. To overcome these limitations, we developed GNNSeq, a novel hybrid machine learning model that integrates a Graph Neural Network (GNN) with Random Forest (RF) and XGBoost. Methods: GNNSeq predicts ligand binding affinity by extracting molecular characteristics and sequence patterns from protein and ligand sequences. The fully optimized GNNSeq model was trained and tested on subsets of the PDB

SUBMITTER: Dandibhotla S 

PROVIDER: S-EPMC11945123 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature