Deep learning has
been successfully applied to structure-based
protein–ligand affinity prediction, yet the black box nature
of these models raises some questions. In a previous study, we presented
K
DEEP
, a convolutional neural network that predicted the
binding affinity of a given protein–ligand complex while reaching
state-of-the-art performance. However, it was unclear what this model
was learning. In this work, we present a new application to visualize
the contribution of each input atom to the prediction made by the
convolutional neural network, aiding in the interpretability of such
predictions. The results suggest that K
DEEP
is able to
learn meaningful chemistry signals from the data, but it has also
exposed the inaccuracies of the current model, serving as a guideline
for further optimization of our prediction tools.