用CNN从红外光谱预测有机分子最高优先级官能团
Deep Convolutional Neural Networks for predicting highest priority functional group in organic molecules
- 用深度卷积神经网络分析红外光谱数据
- 模型准确率高于传统SVM方法
- 适合化学信息学与分子性质预测研究者
本研究旨在预测有机分子中优先级最高的官能团。官能团是由原子组成的特定结构，决定有机分子的物理和化学性质。当分子中存在多个官能团时，主导官能团决定了化合物的整体特性。傅里叶变换红外光谱（FTIR）是一种常用于检测化合物中官能团存在与否的光谱技术。本文提出使用深度卷积神经网络（CNN）从有机分子的傅里叶变换红外光谱（FTIR）中预测最高优先级官能团，并与以往常用的机器学习方法支持向量机（SVM）进行了对比，论证了CNN在该任务中的优越性。
原文摘要 · Abstract (English)
Our work addresses the problem of predicting the highest priority functional group present in an organic molecule. Functional Groups are groups of bound atoms that determine the physical and chemical properties of organic molecules. In the presence of multiple functional groups, the dominant functional group determines the compound's properties. Fourier-transform Infrared spectroscopy (FTIR) is a commonly used spectroscopic method for identifying the presence or absence of functional groups within a compound. We propose the use of a Deep Convolutional Neural Networks (CNN) to predict the highest priority functional group from the Fourier-transform infrared spectrum (FTIR) of the organic molecule. We have compared our model with other previously applied Machine Learning (ML) method Support Vector Machine (SVM) and reasoned why CNN outperforms it.
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