Visualizing Convolutional Neural Network Protein-Ligand Scoring

TL;DR

This paper introduces three visualization methods for 3D CNNs used in protein-ligand scoring, aiding interpretability and improving network design in drug discovery.

Contribution

It presents novel visualization techniques for understanding 3D CNN decisions in protein-ligand scoring, enhancing interpretability and network tuning.

Findings

Three visualization methods for CNN interpretation.

Visualization of convolutional filters and weights.

Improved understanding of CNN decision processes.

Abstract

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.