  Protein-ligand scoring is an important step in a structure-based drug design
pipeline. Selecting a correct binding pose and predicting the binding affinity
of a protein-ligand complex enables effective virtual screening. Machine
learning techniques can make use of the increasing amounts of structural data
that are becoming publicly available. Convolutional neural network (CNN)
scoring functions in particular have shown promise in pose selection and
affinity prediction for protein-ligand complexes. Neural networks are known for
being difficult to interpret. Understanding the decisions of a particular
network can help tune parameters and training data to maximize performance.
Visualization of neural networks helps decompose complex scoring functions into
pictures that are more easily parsed by humans. Here we present three methods
for visualizing how individual protein-ligand complexes are interpreted by 3D
convolutional neural networks. We also present a visualization of the
convolutional filters and their weights. We describe how the intuition provided
by these visualizations aids in network design.

Machine Learning;Machine Learning;Biomolecules