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Fig. 2 | Journal of Cheminformatics

Fig. 2

From: Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity

Fig. 2

Sketch of the Graph neural network iterative process. (a) The \(AGGREGATE^{(l)}_{node}\) function updates node representation vectors by aggregating information coming from itself and its neighbours. (b) As the process iterates, nodes receive information from further nodes in the graph. (c) The \(AGGREGATE^{(l)}_{graph}\) function builds a graph-level representation vector by aggregating information from all nodes. (d) A graph-level representation is learned at each iteration

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Journal of Cheminformatics

ISSN: 1758-2946

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