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Inner and Outer Recursive Neural Networks for Chemoinformatics Applications.

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 Model

journal_title

Journal of chemical information and modeling

authors

Urban G,Subrahmanya N,Baldi P

doi

10.1021/acs.jcim.7b00384

subject

Has Abstract

pub_date

2018-02-26 00:00:00

pages

207-211

issue

2

eissn

1549-9596

issn

1549-960X

journal_volume

58

pub_type

杂志文章

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