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J Chem Inf Model. 2018 Feb 26;58(2):207-211. doi: 10.1021/acs.jcim.7b00384. Epub 2018 Jan 26.

Inner and Outer Recursive Neural Networks for Chemoinformatics Applications.

Author information

1
Department of Computer Science, University of California, Irvine , Irvine, California 92697, United States.
2
ExxonMobil Research and Engineering , Annandale, New Jersey 08801, United States.

Abstract

Deep learning methods applied to problems in chemoinformatics often require the use of recursive neural networks to handle data with graphical structure and variable size. We present a useful classification of recursive neural network approaches into two classes, the inner and outer approach. The inner approach uses recursion inside the underlying graph, to essentially "crawl" the edges of the graph, while the outer approach uses recursion outside the underlying graph, to aggregate information over progressively longer distances in an orthogonal direction. We illustrate the inner and outer approaches on several examples. More importantly, we provide open-source implementations [available at www.github.com/Chemoinformatics/InnerOuterRNN and cdb.ics.uci.edu ] for both approaches in Tensorflow which can be used in combination with training data to produce efficient models for predicting the physical, chemical, and biological properties of small molecules.

PMID:
29320180
DOI:
10.1021/acs.jcim.7b00384
[Indexed for MEDLINE]

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