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.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Urban G,Subrahmanya N,Baldi Pdoi
10.1021/acs.jcim.7b00384subject
Has Abstractpub_date
2018-02-26 00:00:00pages
207-211issue
2eissn
1549-9596issn
1549-960Xjournal_volume
58pub_type
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