Fig. 2: Architecture of the interpretable CNN-based multi-classification model proposed by the work.

a A CNN-based multi-classifier. Each conformation is represented by a pixel map through a matrix transformation, and the CNN-based classification model is trained based on the pixel maps and their category labels inferred from the clustering analysis. b LIME interpreter for the CNN model. Based on a locally linear approximation paradigm, a LIME interpreter is developed to identify important residues deciding the CNN classification result. In the picture of “local linear approximation,” salmon, blue, and yellow backgrounds represent three classification classes, while salmon crosses, blue triangles, and yellow stars represent the conformation samples of the three different classes, respectively. For example, the star highlighted in red line represents a conformation sample being explained, around which the perturbed dataset represented by yellow stars is generated by adding perturbations. The star sizes denote the proximity measure between the perturbed sample and the sample being explained. The gray dotted line represents the local linear model that is trained on the perturbed dataset. LIME matrixes are then generated for each class to evaluate the importance of each pixel in deciding the specific class. By projecting the important pixels into the corresponding atoms, important residues can be identified.