Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks.
J Chem Inf Model
; 64(14): 5439-5450, 2024 Jul 22.
Article
em En
| MEDLINE
| ID: mdl-38953560
ABSTRACT
Message passing neural networks (MPNNs) on molecular graphs generate continuous and differentiable encodings of small molecules with state-of-the-art performance on protein-ligand complex scoring tasks. Here, we describe the proximity graph network (PGN) package, an open-source toolkit that constructs ligand-receptor graphs based on atom proximity and allows users to rapidly apply and evaluate MPNN architectures for a broad range of tasks. We demonstrate the utility of PGN by introducing benchmarks for affinity and docking score prediction tasks. Graph networks generalize better than fingerprint-based models and perform strongly for the docking score prediction task. Overall, MPNNs with proximity graph data structures augment the prediction of ligand-receptor complex properties when ligand-receptor data are available.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Proteínas
/
Redes Neurais de Computação
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article