Activity prediction of aminoquinoline drugs based on deep learning,Biotechnology and Applied Biochemistry

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Activity prediction of aminoquinoline drugs based on deep learning
Biotechnology and Applied Biochemistry ( IF 3.2 ) Pub Date : 2020-08-31 , DOI: 10.1002/bab.2016
Wenle Wang ₁ , Jinquan Chen ₁ , Yujie Zhu ₁ , Feng Feng ₁

Affiliation

The results of the traditional prediction method for the activity of aminoquinoline drugs are inaccurate, so the prediction method for the activity of aminoquinoline drugs based on the deep learning is designed. The molecular holographic distance vector method was used to describe the molecular structure of 40 aminoquinoline compounds, and the principal component regression method was used for modeling and quantitative analysis. Two methods were used to predict the activity of aminoquinoline drugs. The correlation coefficients of the results obtained from the two sets of activity data and the cross test were 0.9438 and 0.9737, and 0.8305 and 0.9098, respectively. Our data suggested that method for the activity prediction of aminoquinoline drugs based on deep learning studied in this paper can better predict the activity of aminoquinoline drugs and provide a strong basis for the activity prediction of aminoquinoline drugs.

中文翻译：

基于深度学习的氨基喹啉类药物活性预测

传统的氨基喹啉类药物活性预测方法结果不准确，设计了基于深度学习的氨基喹啉类药物活性预测方法。采用分子全息距离矢量法描述40种氨基喹啉化合物的分子结构，采用主成分回归法进行建模和定量分析。两种方法用于预测氨基喹啉药物的活性。两组活动数据和交叉检验所得结果的相关系数分别为0.9438和0.9737，0.8305和0.9098。

更新日期：2020-08-31

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