Visualization of Convolutional Neural Network...

RelevanceHow the CNN scores the unchanged structure

Relevance propagation propagates the classifieroutput throughout the network as a relevancequantity. At each network level the total relevanceis conserved.

The negative and positive components of therelevance are treated separately.

Pros:• Implemented in a single backwards test• May better delineate classifier decision boundary• Separates positive and negative effects• Volumetric visualization• Explains current scoreCons:• Custom implementation for each network layer• Does not extrapolate beyond input• No directionality

GradientsHow the CNN changes the structure to score better

Backpropagation propagates the gradient of theloss throughout the network and can ultimatelycalculate the gradient with respect to the inputvalues.

Pros:• Implemented in a single backwards test• Indicates how to improve structure• Volumetric visualization• Provides directionalityCons:• Intermixes positive and negative effects• Does not explain current score

MaskingHow the CNN scores changes to the structure

Atom importance is determined by the change inscore upon removal of the atom, either individuallyor as part of molecular fragments or proteinresidues.

Pros:• Easy to implement – no need to modify CNN• Can evaluate large modifications (fragments)Cons:• Evaluates non-physical structures• Inefficient – requires many CNN evaluations• No volumetric output

This work is supported by R01GM108340 from the National Institute of General Medical Sciences.

Visualization of Convolutional Neural Network Scoring of Protein-Ligand BindingJoshua Hochuli1,2, Matthew Ragoza3, and David Ryan Koes3

1Department of Computer Science, 2Department of Biological Sciences, 3Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA

AbstractConvolutional neural networks provide a promisingapproach to scoring protein-ligand interactions. Neuralnetworks are inherently difficult to analyze andunderstand, often being called 'black boxes'. Running agiven protein-ligand complex through a network simplyproduces a value from 0 to 1 to represent the probabilitythat the pose is correct, without providing any insight asto how that number was generated. Visualizations of theneural network's decision-making process allow for theanalysis of its understanding of chemical interactions.We describe multiple visualization workflows and provideexamples of how the resulting visualizations can aid inbiological and chemical understanding of theinteraction while also discussing current limitations of themethod.

Background

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. Bind: 0.99Not Bind: 0.01

Convolution Feature Maps

Convolution Feature Maps

Fully ConnectedTraditional NN

VoxelizedInput

The ligand and receptoratoms of a complex arerepresented as Gaussiandensities of atom types.

Atom densities are voxelized to a grid that is the input toa 3D convolutional neural network. The network is trainedto distinguish between low (<2Å) RMSD and high (>4Å)RMSD docked poses generated using AutoDock Vinaand the 4056 structures of the refined set of PDBbind.

The trained network substantially outperforms AutoDockVina at selecting and ranking poses. Visualizations of thenetwork provide insights into what the network haslearned and how it processes specific features.

Compute output error

Backpropagate

Calculate gradient with respect to input

ReferencesRagoza, M., Hochuli, J., Idrobo, E., Sunseri, J. and Koes, D.R., 2016. Protein-Ligand Scoring with Convolutional Neural Networks. arXivpreprint arXiv:1612.02751Samek, W., Binder, A., Montavon, G., Lapuschkin, S. and Müller, K.R., 2016. Evaluating the visualization of what a deep neural network has learned. IEEE Transactions on Neural Networks and Learning Systems.Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R. and Samek, W., 2015. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS one, 10(7), p.e0130140.

2IDZScore: 0.04

2FV5Score: 0.24

1UGXScore: 0.62

3EJTScore: 0.92

phosphorus phosphorus

oxygen donor oxygen donor

oxygen acceptor oxygen acceptor

oxygen donor oxygen donor

1YZ3Score: 0.99

nitrogen donor nitrogen donor

oxygen acceptor oxygen acceptor

2FV5Score: 0.24

1UGXScore: 0.62

3EJTScore: 0.92