A deep learning architecture for metabolic pathway prediction
A deep learning architecture for metabolic pathway prediction
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DOI：
10.1093/bioinformatics/btz954
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发表时间：
2020-04-15
期刊：
影响因子：
5.8
通讯作者：
Hero, Alfred O.
中科院分区：
文献类型：
--
作者：
Baranwal, Mayank;Magner, Abram;Hero, Alfred O.
动机：了解分子和途径类之间的机制和结构映射对于设计用于合成新分子的反应预测因子至关重要。这篇文章研究的问题，预测类代谢途径（一系列的化学反应发生在一个细胞内），其中一个给定的生化化合物参与。我们应用一种混合机器学习方法，该方法由用于提取分子形状特征的图卷积网络组成，作为随机森林分类器的输入。与以前应用于此问题的机器学习方法相比，我们的框架直接从输入SMILES表示中自动提取相关的形状特征，SMILES表示是组成分子的化学结构的原子-键规范。我们的方法能够正确预测95.16%的测试化合物的相应代谢途径类别，而竞争的方法只能达到84.92%或更低的准确率。此外，我们的框架扩展到多个途径类中具有混合成员的化合物的分类任务。我们对这个多标签任务的预测准确率为97.61%。我们分析了各种全球物理化学特征的路径类预测问题的相对重要性，并表明简单的线性/逻辑回归模型可以预测这些全球特征的值，从使用我们的框架提取的形状特征。
Motivation: Understanding the mechanisms and structural mappings between molecules and pathway classes are critical for design of reaction predictors for synthesizing new molecules. This article studies the problem of prediction of classes of metabolic pathways (series of chemical reactions occurring within a cell) in which a given biochemical compound participates. We apply a hybrid machine learning approach consisting of graph convolutional networks used to extract molecular shape features as input to a random forest classifier. In contrast to previously applied machine learning methods for this problem, our framework automatically extracts relevant shape features directly from input SMILES representations, which are atom-bond specifications of chemical structures composing the molecules.Results: Our method is capable of correctly predicting the respective metabolic pathway class of 95.16% of tested compounds, whereas competing methods only achieve an accuracy of 84.92% or less. Furthermore, our framework extends to the task of classification of compounds having mixed membership in multiple pathway classes. Our prediction accuracy for this multi-label task is 97.61%. We analyze the relative importance of various global physicochemical features to the pathway class prediction problem and show that simple linear/logistic regression models can predict the values of these global features from the shape features extracted using our framework.