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A Prediction Method of Binding Affinity Based on Deep Multiscale Graph Neural Networks for Designing Polyphenol-Protein Complexes Delivery Systems
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Application of Molecular Transformer approach for predicting the potential reactions to generate advanced glycation end products in infant formula
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Molecular structure-affinity relationship of selected phenolic compounds for lupin seed γ-conglutin
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Molecular dynamics simulation of the interactions between sesamol and myosin combined with spectroscopy and molecular docking studies
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Dynamic gastric stability and in vitro lipid digestion of soybean protein isolate and three storage protein-stabilized emulsions: Effects of ultrasonic treatment
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In silico prediction of the β-cyclodextrin complexation based on Monte Carlo method
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Ligand-Induced Conformational Changes: Improved Predictions of Ligand Binding Conformations and Affinities
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Sesamol: a powerful functional food ingredient from sesame oil for cardioprotection
inferredcites →https://doi.org/10.1080/10408398.2010.499017openalex-extracted
A Review of Dietary Polyphenol-Plasma Protein Interactions: Characterization, Influence on the Bioactivity, and Structure-Affinity Relationship
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Application of Molecular Transformer Approach for Predicting the Potential Reactions to Generate Advanced Glycation End Products in Infant Formula
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SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules
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