Fig. 3: Performance of the atom-level structure learning of the SpHN-VDA to extract drug features for VDA prediction. | Communications Biology

Fig. 3: Performance of the atom-level structure learning of the SpHN-VDA to extract drug features for VDA prediction.

From: A spatial hierarchical network learning framework for drug repositioning allowing interpretation from macro to micro scale

Fig. 3

a The average ROC curves based on fivefold cross-validation of VDA prediction with the different message-passing structures in the HDVD and VDA2 datasets show the performance of SpHN-VDA compared to variant methods containing SpHN-VDA_atomGCN, SpHN-VDA_w/o_3DAttention, and SpHN-VDA_w/o_3DInformation. b The correlation of the heatmap of each atomic feature under SpHN-VDA training, SpHN-VDA_atomGCN training, and random generation without training. The color of each pixel is determined by the Pearson correlation coefficient of the corresponding pairwise atom features. Red indicates a high value of the Pearson correlation coefficient, and green indicates a low value. The larger the number of high values represents the powerful ability of model capturing long-range dependence. c The binding sites for the HIV-1 protease IRM mutant (PDB id: 2FXD) with atazanavir and the predicted critical motifs of atazanavir. The contribution of these motifs is presented as a heatmap, where color depth is positively correlated to the z score. The top three motifs were confirmed to maintain the corresponding binding sites of GLY-48 and GLY-27. Source data are provided as a Source Data file in Supplementary Data 4.

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