graphLambda: Fusion Graph Neural Networks for Binding Affinity Prediction.
J Chem Inf Model
; 64(7): 2323-2330, 2024 Apr 08.
Article
em En
| MEDLINE
| ID: mdl-38366974
ABSTRACT
Predicting the binding affinity of protein-ligand complexes is crucial for computer-aided drug discovery (CADD) and the identification of potential drug candidates. The deep learning-based scoring functions have emerged as promising predictors of binding constants. Building on recent advancements in graph neural networks, we present graphLambda for protein-ligand binding affinity prediction, which utilizes graph convolutional, attention, and isomorphism blocks to enhance the predictive capabilities. The graphLambda model exhibits superior performance across CASF16 and CSAR HiQ NRC benchmarks and demonstrates robustness with respect to different types of train-validation set partitions. The development of graphLambda underscores the potential of graph neural networks in advancing binding affinity prediction models, contributing to more effective CADD methodologies.
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Base de dados:
MEDLINE
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Proteínas
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Redes Neurais de Computação
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article