TY - JOUR
T1 - CurvAGN
T2 - Curvature-based Adaptive Graph Neural Networks for Predicting Protein-Ligand Binding Affinity
AU - Wu, Jianqiu
AU - Chen, Hongyang
AU - Cheng, Minhao
AU - Xiong, Haoyi
N1 - Publisher Copyright:
© 2023, BioMed Central Ltd., part of Springer Nature.
PY - 2023/12
Y1 - 2023/12
N2 - Accurately predicting the binding affinity between proteins and ligands is crucial for drug discovery. Recent advances in graph neural networks (GNNs) have made significant progress in learning representations of protein-ligand complexes to estimate binding affinities. To improve the performance of GNNs, there frequently needs to look into protein-ligand complexes from geometric perspectives. While the “off-the-shelf” GNNs could incorporate some basic geometric structures of molecules, such as distances and angles, through modeling the complexes as homophilic graphs, these solutions seldom take into account the higher-level geometric attributes like curvatures and homology, and also heterophilic interactions.To address these limitations, we introduce the Curvature-based Adaptive Graph Neural Network (CurvAGN). This GNN comprises two components: a curvature block and an adaptive attention guided neural block (AGN). The curvature block encodes multiscale curvature informaton, then the AGN, based on an adaptive graph attention mechanism, incorporates geometry structure including angle, distance, and multiscale curvature, long-range molecular interactions, and heterophily of the graph into the protein-ligand complex representation. We demonstrate the superiority of our proposed model through experiments conducted on the PDBbind-V2016 core dataset.
AB - Accurately predicting the binding affinity between proteins and ligands is crucial for drug discovery. Recent advances in graph neural networks (GNNs) have made significant progress in learning representations of protein-ligand complexes to estimate binding affinities. To improve the performance of GNNs, there frequently needs to look into protein-ligand complexes from geometric perspectives. While the “off-the-shelf” GNNs could incorporate some basic geometric structures of molecules, such as distances and angles, through modeling the complexes as homophilic graphs, these solutions seldom take into account the higher-level geometric attributes like curvatures and homology, and also heterophilic interactions.To address these limitations, we introduce the Curvature-based Adaptive Graph Neural Network (CurvAGN). This GNN comprises two components: a curvature block and an adaptive attention guided neural block (AGN). The curvature block encodes multiscale curvature informaton, then the AGN, based on an adaptive graph attention mechanism, incorporates geometry structure including angle, distance, and multiscale curvature, long-range molecular interactions, and heterophily of the graph into the protein-ligand complex representation. We demonstrate the superiority of our proposed model through experiments conducted on the PDBbind-V2016 core dataset.
UR - http://www.scopus.com/inward/record.url?scp=85173612954&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85173612954&partnerID=8YFLogxK
U2 - 10.1186/s12859-023-05503-w
DO - 10.1186/s12859-023-05503-w
M3 - Article
C2 - 37798653
AN - SCOPUS:85173612954
SN - 1471-2105
VL - 24
JO - BMC bioinformatics
JF - BMC bioinformatics
IS - 1
M1 - 378
ER -