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Figure 1

From: Predicting target–ligand interactions with graph convolutional networks for interpretable pharmaceutical discovery

Figure 1

PLA-Net workflow. Schematic representation of a PLA-Net model for predicting interactions between small organic molecules and one of the 102 target proteins in the AD dataset. Graph representations of the molecule and a given target protein are generated from SMILES and FASTA sequences and are used as input to the Ligand Module (LM) and Protein Module (PM), respectively. Each module comprises a deep GCN followed by an average pooling layer, which extracts relevant features of their corresponding input graph. Both representations are finally concatenated and combined through a fully connected layer to predict the target–ligand interaction probability. Created with BioRender.com.

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