In silico prediction of drug‐induced ototoxicity using machine learning and deep learning methods

2021 ◽  
Author(s):  
Xin Huang ◽  
Fang Tang ◽  
Yuqing Hua ◽  
Xiao Li
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
In Silico ◽  
In Silico Prediction ◽  
Learning Methods ◽  
Drug Induced

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

In silico prediction of drug-induced developmental toxicity by using machine learning approaches

2019 ◽  
Vol 24 (4) ◽  
pp. 1281-1290
Author(s):  
Hui Zhang ◽  
Jun Mao ◽  
Hua-Zhao Qi ◽  
Lan Ding
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
Developmental Toxicity ◽  
Learning Approaches ◽  
In Silico Prediction ◽  
Drug Induced

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.

In silico prediction of drug‐induced rhabdomyolysis with machine‐learning models and structural alerts

2019 ◽  
Vol 39 (8) ◽  
pp. 1224-1232 ◽  
Author(s):  
Xueyan Cui ◽  
Juan Liu ◽  
Jinfeng Zhang ◽  
Qiuyun Wu ◽  
Xiao Li
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
In Silico Prediction ◽  
Learning Models ◽  
Drug Induced ◽  
Structural Alerts ◽  
Machine Learning Models

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.

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