Activity prediction of aminoquinoline drugs based on deep learning

被引：1

作者：

Wang, Wenle ^{[1
]}

Chen, Jinquan ^{[1
]}

Zhu, Yujie ^{[1
]}

Feng, Feng ^{[1
]}

机构：

[1] Jiangsu Food & Pharmaceut Sci Coll, Dept Mech & Elect Engn, Huaian 223005, Peoples R China

来源：

BIOTECHNOLOGY AND APPLIED BIOCHEMISTRY | 2021年 / 68卷 / 04期

关键词：

activity; aminoquinoline; deep learning; prediction; AMINO-ACID; QUINOLINE; CHLOROQUINE; QSAR;

D O I：

10.1002/bab.2016

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：927 / 937

页数：11

共 50 条

[21] Key amino acid residues conferring enhanced enzyme activity at cold temperatures in an Antarctic polyextremophilic β-galactosidase [J].

Laye, Victoria J. ;

Karan, Ram ;

Kim, Jong-Myoung ;

Pecher, Wolf T. ;

DasSarma, Priya ;

DasSarma, Shiladitya .

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2017, 114 (47) :12530-12535

[22] 1H NMR-based Investigation of Metabolic Response to Electro-Acupuncture Stimulation [J].

Lin, Caigui ;

Wei, Zhiliang ;

Cheng, Kian-Kai ;

Xu, Jingjing ;

Shen, Guiping ;

She, Chang ;

Zhong, Huan ;

Chang, Xiaorong ;

Dong, Jiyang .

SCIENTIFIC REPORTS, 2017, 7

[23] PESTICIDE TOXICITY: A MECHANISTIC APPROACH [J].

Lushchak, Volodymyr I. ;

Matviishyn, Tetiana M. ;

Husak, Viktor V. ;

Storey, Janet M. ;

Storey, Kenneth B. .

EXCLI JOURNAL, 2018, 17 :1101-1136

[24] Helicobacter pylori chemoreceptor TlpC mediates chemotaxis to lactate [J].

Machuca, Mayra A. ;

Johnson, Kevin S. ;

Liu, Yu C. ;

Steer, David L. ;

Ottemann, Karen M. ;

Roujeinikova, Anna .

SCIENTIFIC REPORTS, 2017, 7

[25] A comparison of the proximate, fibre, mineral content, amino acid and the fatty acid profile of Marula (Sclerocarya birrea caffra) nut and soyabean (Glycine max) meals [J].

Malebana, Ingrid M. M. ;

Nkosi, Bhutikini D. ;

Erlwanger, Kennedy H. ;

Chivandi, Eliton .

JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, 2018, 98 (04) :1381-1387

[26]

Mandewale M. C., 2017, BJBAS, V6, P354, DOI [10.1016/j.bjbas.2017.07.005, DOI 10.1016/J.BJBAS.2017.07.005]

[27]

Meng S.Y., 2018, CCAMLR SCI, V25, P97

[28] Deep Learning-driven research for drug discovery: Tackling Malaria [J].

Neves, Bruno J. ;

Braga, Rodolpho C. ;

Alves, Vinicius M. ;

Lima, Marilia N. N. ;

Cassiano, Gustavo C. ;

Muratov, Eugene N. ;

Costa, Fabio T. M. ;

Andrade, Carolina Horta .

PLOS COMPUTATIONAL BIOLOGY, 2020, 16 (02)

[29] Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey [J].

Nguyen, Giang ;

Dlugolinsky, Stefan ;

Bobak, Martin ;

Viet Tran ;

Lopez Garcia, Alvaro ;

Heredia, Ignacio ;

Malik, Peter ;

Hluchy, Ladislav .

ARTIFICIAL INTELLIGENCE REVIEW, 2019, 52 (01) :77-124

[30] Cellular responses to human cytomegalovirus infection: Induction of a mesenchymal-to-epithelial transition (MET) phenotype [J].

Oberstein, Adam ;

Shenk, Thomas .

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2017, 114 (39) :E8244-E8253

← 1 2 3 4 5 →