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Biochemical Research Methods
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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
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Summary: In this study, a deep multiscale graph neural network called MGraphDTA was proposed for predicting drug-target affinity (DTA). The method combines dense connection and super-deep GNN to capture the local and global structure of compounds simultaneously. A novel visual explanation method, Grad-AAM, was also developed to analyze the model from a chemical perspective.
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Biochemical Research Methods
Tri Minh Nguyen et al.
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Shaofu Lin et al.
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Summary: This study proposes a novel deep learning-based approach called DLSSAffinity to accurately predict protein-ligand binding affinity. Unlike existing methods, DLSSAffinity combines pocket-ligand structural pairs, full-length protein sequences, and ligand SMILES to improve prediction accuracy.
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Summary: Accurately predicting protein-ligand binding affinities is crucial in drug discovery. While current methods face challenges, fusion models that combine features and inference from complementary representations show improved prediction accuracy. Comparative analysis reveals that fusion models perform better than individual neural network models, docking scoring, and MM/GBSA calculations, with the added benefit of greater computational efficiency.
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Asad Ahmed et al.
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Summary: Drug repurposing, finding new uses for already approved drugs, is a cost-effective approach to drug development. The GraphDTA model represents drugs as graphs and utilizes graph neural networks to predict drug-target affinity more effectively than non-deep learning models. This demonstrates the potential of deep learning models in predicting drug-target binding affinity and the benefits of representing drugs as graphs.
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