Supplementary Figure 5: Performance of MaSIF-search fingerprints under different shape complementarity filters for the interacting patches, and effect of inverting input features.

a. We set up three classes of interacting patches, filtered by shape complementarity, and trained neural networks with each set. The sets are illustrated here with three examples, where the surface is colored according to shape complementarity from white (0.0) to red (1.0). b. Descriptor distance distribution plot for interacting and non-interacting patches depending on the shape complementarity class. c. ROC AUC values for the GIF descriptors, MaSIF descriptors trained only on geometry, chemistry, or both, and patches found in unbound proteins within each complementarity class (G+C ub). # of pairs of patches: high comp, 38038 positives and 38038 negatives; low comp.: 16798 positives and 16798 negatives; low comp. 21297 positive and 21297 negatives. d, e. MaSIF-search benefits from the inversion of features in the input. d. ROC AUCs of a network trained/tested with inversion (green) vs. a network trained/tested without inversion (blue) using both Geometric (G) and chemical (C) features. The plot’s ROC curve was computed on 13338 positive and 13338 negative pairs of samples. e. Performance of a network where electrostatics and the hbond features were inverted (green) vs. one in which they were not (blue), on a network trained with only chemical features.