Exploring Graph Traversal Algorithms in Graph-Based Molecular Generation
Journal article, 2022

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 data set of natural products. These metrics include percent validity, molecular coverage, and molecular shape. We also observe that by using either a breadth- or depth-first traversal it is possible to overtrain the generative models, at which point the results with either graph traversal algorithm are identical.

Author

Rocio Mercado

AstraZeneca AB

Esben Jannik Bjerrum

AstraZeneca AB

Ola Engkvist

AstraZeneca AB

Chalmers, Computer Science and Engineering (Chalmers)

Journal of Chemical Information and Modeling

1549-9596 (ISSN) 1549960x (eISSN)

Vol. 62 9 2093-2100

Subject Categories

Bioinformatics (Computational Biology)

Geophysics

Geosciences, Multidisciplinary

DOI

10.1021/acs.jcim.1c00777

PubMed

34757744

More information

Latest update

7/23/2024