Activity prediction of aminoquinoline drugs based on deep learning. (11th October 2020)

• Record Type: Journal Article
• Authors: Wang, Wenle; Chen, Jinquan; Zhu, Yujie; Feng, Feng
• Is Part Of: Biotechnology and applied biochemistry. Volume 68:Number 4(2021)
• Page Start: 927
• Page End: 937
• Publication Date: 2020-10-11
• Subjects: activity -- aminoquinoline -- deep learning -- prediction; Biotechnology -- Periodicals; Biochemical engineering -- Periodicals; Biochemistry -- Periodicals; Biochemistry -- Periodicals; Genetic Techniques -- Periodicals; Microbiological Techniques -- Periodicals; 660.6
• DOI: 10.1002/bab.2016
• Languages: English
• ISSNs: 0885-4513

Abstract

Abstract: 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. Abstract : Chemical structure of aminoquinoline drugs.