Fmgnn: a method to predict compound-protein interaction with pharmacophore features and physicochemical properties of amino acids

HIGHLIGHTS

• who: Chunyan Tang and collaborators from the (UNIVERSITY) have published the article: FMGNN: A Method to Predict Compound-Protein Interaction With Pharmacophore Features and Physicochemical Properties of Amino Acids, in the Journal: (JOURNAL)
• what: The authors propose a novel hybrid model with Factorization Machines and Graph Neural Network called FMGNN to extract the low-order and high-order features respectively. The authors design a compound-protein interactions (CPIs) prediction method with pharmacophore features of compound and physicochemical properties of amino acids. The authors propose a novel model called FMGNN that integrates the architectures of factorization . . .
  

   

  

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