# Inner and Outer Recursive Neural Networks for Chemoinformatics Applications

journal contribution

posted on 2018-01-10, 00:00 authored by Gregor Urban, Niranjan Subrahmanya, Pierre BaldiDeep 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.