scholarly journals In silico prediction of chemical genotoxicity using machine learning methods and structural alerts

2018 ◽  
Vol 7 (2) ◽  
pp. 211-220 ◽  
Author(s):  
Defang Fan ◽  
Hongbin Yang ◽  
Fuxing Li ◽  
Lixia Sun ◽  
Peiwen Di ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
In Silico Prediction ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Structural Alerts

Six machine learning methods combined with six fingerprints and 49 descriptors were utilized to predict chemical genotoxicity.

In Silico Prediction of Metabolic Epoxidation for Drug‐like Molecules via Machine Learning Methods

2020 ◽  
Vol 39 (8) ◽  
pp. 1900178
Author(s):  
Jiajing Hu ◽  
Yingchun Cai ◽  
Weihua Li ◽  
Guixia Liu ◽  
Yun Tang
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
In Silico Prediction ◽  
Learning Methods ◽  
Machine Learning Methods

In Silico Prediction of CYP2C8 Inhibition with Machine-Learning Methods

2021 ◽  
Author(s):  
Xiaoxiao Zhang ◽  
Piaopiao Zhao ◽  
Zhiyuan Wang ◽  
Xuan Xu ◽  
Guixia Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
In Silico Prediction ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals In silico prediction of hERG potassium channel blockage by chemical category approaches

2016 ◽  
Vol 5 (2) ◽  
pp. 570-582 ◽  
Author(s):  
Chen Zhang ◽  
Yuan Zhou ◽  
Shikai Gu ◽  
Zengrui Wu ◽  
Wenjie Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Potassium Channel ◽  
In Silico ◽  
Molecular Descriptors ◽  
In Silico Prediction ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Channel Blockage

A series of models of hERG blockage were built using five machine learning methods based on 13 molecular descriptors, five types of fingerprints and molecular descriptors combining fingerprints at four blockage thresholds.

scholarly journals In silico prediction of serious eye irritation or corrosion potential of chemicals

RSC Advances ◽  
2017 ◽  
Vol 7 (11) ◽  
pp. 6697-6703 ◽  
Author(s):  
Qin Wang ◽  
Xiao Li ◽  
Hongbin Yang ◽  
Yingchun Cai ◽  
Yinyin Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
Corrosion Potential ◽  
Binary Classification ◽  
Classification Models ◽  
In Silico Prediction ◽  
Learning Methods ◽  
Eye Irritation ◽  
Machine Learning Methods ◽  
Chemical Fingerprints

Chemical fingerprints combined with machine learning methods were used to build binary classification models for predicting the potential EC/EI of compounds.

scholarly journals In silico prediction of chemical aquatic toxicity for marine crustaceans via machine learning

2019 ◽  
Vol 8 (3) ◽  
pp. 341-352 ◽  
Author(s):  
Lin Liu ◽  
Hongbin Yang ◽  
Yingchun Cai ◽  
Qianqian Cao ◽  
Lixia Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
Aquatic Toxicity ◽  
In Silico Prediction ◽  
Chemical Toxicity ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Marine Crustaceans

Six machine learning methods combined with descriptors or fingerprints were employed to predict chemical toxicity on marine crustaceans.

In Silico Prediction of Chemicals Binding to Aromatase with Machine Learning Methods

2017 ◽  
Vol 30 (5) ◽  
pp. 1209-1218 ◽  
Author(s):  
Hanwen Du ◽  
Yingchun Cai ◽  
Hongbin Yang ◽  
Hongxiao Zhang ◽  
Yuhan Xue ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
In Silico Prediction ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals In silico prediction of pesticide aquatic toxicity with chemical category approaches

2017 ◽  
Vol 6 (6) ◽  
pp. 831-842 ◽  
Author(s):  
Fuxing Li ◽  
Defang Fan ◽  
Hao Wang ◽  
Hongbin Yang ◽  
Weihua Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
Aquatic Toxicity ◽  
In Silico Prediction ◽  
Learning Methods ◽  
Machine Learning Methods

Herein, six machine learning methods combined with nine fingerprints were used to predict aquatic toxicity of pesticides.

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