Exploring graph traversal algorithms in graph-based molecular generation

20 September 2021, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


Here we explore the impact of different graph traversal algorithms on molecular graph generation. We do this by training a graph-based deep molecular generative model to build structures using a node order determined via either a breadth- or depth-first search algorithm. What we observe is that using a breadth-first traversal leads to better coverage of training data features compared to a depth-first traversal. We have quantified these differences using a variety of metrics on a dataset of natural products. These metrics include: percent validity, molecular coverage, and molecular shape. We also observe that using either a breadth- or depth-first traversal it is possible to over-train the generative models, at which point the results with the graph traversal algorithm are identical


graph traversal algorithms
deep learning
deep molecular generative models
molecular design

Supplementary weblinks


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