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Convolutional neural networks provide a promising approach to scoring protein-ligand interactions. Neural networks are inherently difficult to analyze and understand, often being called'black boxes'. Running a given protein-ligand complex through a network simply produces a value from 0 to 1 to represent the probability that the pose is correct, without providing any insight as to how that number was generated. Visualizations of the neural network's decision-making process allow for the analysis of its understanding of chemical interactions. We describe multiple visualization workflows and provide examples of how the resulting visualizations can aid in biological and chemical understanding of the interaction while also discussing current limitations of the method.