Inner and Outer Recursive Neural Networks for Chemoinformatics Applications,Journal of Chemical Information and Modeling

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Inner and Outer Recursive Neural Networks for Chemoinformatics Applications
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2018-01-26 00:00:00 , DOI: 10.1021/acs.jcim.7b00384
Gregor Urban ₁ , Niranjan Subrahmanya ₂ , Pierre Baldi ₁

Affiliation

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.

中文翻译：

用于化学信息学应用的内部和外部递归神经网络

应用于化学信息学问题的深度学习方法通常需要使用递归神经网络来处理具有图形结构和可变大小的数据。我们将递归神经网络方法的有用分类分为两类，内部方法和外部方法。内部方法使用基础图内部的递归来实质上“爬网”图的边缘，而外部方法使用基础图外部的递归来在正交方向上逐渐变长的距离上聚合信息。我们通过几个示例说明内部和外部方法。更重要的是，我们提供了开放源代码实现[可从www.github.com/Chemoinformatics/InnerOuterRNN和cdb.ics.uci获得。

更新日期：2018-01-26

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