Figure shows a few channels activations, further grouped by the corresponding convolutional block. This example supports our intuition: while filters in early layers reduce to node, angle, and edge detectors, filters in deeper layers are activated by larger sub-structures in the molecule, e.g., rings and functional groups. It is then natural to use these layer attributions as a chemically meaningful feature basis for our explanations. Thus, we compute feature attributions values for each convolutional layer of the network Φ, choosing gradients to measure feature importance. Explicitly, for each convolutional layer p we compute superpixel attributions as