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

Hui Zhang; Jun Mao; Hua-Zhao Qi; Lan Ding

doi:10.1007/s11030-019-09991-y

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

Molecular Diversity ◽

10.1007/s11030-019-09991-y ◽

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

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

Journal of Applied Toxicology ◽

10.1002/jat.3808 ◽

2019 ◽

Vol 39 (8) ◽

pp. 1224-1232 ◽

Cited By ~ 1

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

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

Chemical Biology & Drug Design ◽

10.1111/cbdd.13894 ◽

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

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In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2013.01.015 ◽

2013 ◽

Vol 43 (4) ◽

pp. 395-404 ◽

Cited By ~ 12

Author(s):

Bing-Ke Li ◽

Yong Cong ◽

Xue-Gang Yang ◽

Ying Xue ◽

Yi-Zong Chen

Keyword(s):

Machine Learning ◽

Tyrosine Kinase ◽

Tyrosine Kinase Inhibitors ◽

In Silico ◽

Kinase Inhibitors ◽

Molecular Descriptor ◽

Recursive Feature Elimination ◽

Learning Approaches ◽

In Silico Prediction ◽

Elimination Method

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In silico prediction of chemical respiratory toxicity via machine learning

Computational Toxicology ◽

10.1016/j.comtox.2021.100155 ◽

2021 ◽

Vol 18 ◽

pp. 100155

Author(s):

Zhiyuan Wang ◽

Piaopiao Zhao ◽

Xiaoxiao Zhang ◽

Xuan Xu ◽

Weihua Li ◽

...

Keyword(s):

Machine Learning ◽

In Silico ◽

In Silico Prediction ◽

Respiratory Toxicity

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Development and evaluation of in silico prediction model for drug-induced respiratory toxicity by using naïve Bayes classifier method

Food and Chemical Toxicology ◽

10.1016/j.fct.2018.09.051 ◽

2018 ◽

Vol 121 ◽

pp. 593-603 ◽

Cited By ~ 9

Author(s):

Hui Zhang ◽

Jin-Xiang Ma ◽

Chun-Tao Liu ◽

Ji-Xia Ren ◽

Lan Ding

Keyword(s):

Prediction Model ◽

In Silico ◽

Naive Bayes ◽

Naïve Bayes ◽

Naive Bayes Classifier ◽

In Silico Prediction ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Drug Induced ◽

Respiratory Toxicity

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

Molecular Informatics ◽

10.1002/minf.201900178 ◽

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

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In silico identification of human pregnane X receptor activators from molecular descriptors by machine learning approaches

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2012.05.012 ◽

2012 ◽

Vol 118 ◽

pp. 271-279 ◽

Cited By ~ 6

Author(s):

Hanbing Rao ◽

Yanying Wang ◽

Xianyin Zeng ◽

Xianxiang Wang ◽

Yong Liu ◽

...

Keyword(s):

Machine Learning ◽

In Silico ◽

Molecular Descriptors ◽

Pregnane X Receptor ◽

Learning Approaches ◽

In Silico Identification

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In silico prediction of drug-induced liver injury: Quo vadis?

Advances in Molecular Toxicology ◽

10.1016/b978-0-444-64293-6.00002-6 ◽

2020 ◽

pp. 31-68

Author(s):

Ching-Feng Weng ◽

Max K. Leong

Keyword(s):

Liver Injury ◽

In Silico ◽

In Silico Prediction ◽

Drug Induced ◽

Drug Induced Liver Injury ◽

Quo Vadis

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In Silico Prediction of CYP2C8 Inhibition with Machine-Learning Methods

Chemical Research in Toxicology ◽

10.1021/acs.chemrestox.1c00078 ◽

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

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In silico prediction of the developmental toxicity of diverse organic chemicals in rodents for regulatory purposes

Toxicology Research ◽

10.1039/c5tx00493d ◽

2016 ◽

Vol 5 (3) ◽

pp. 773-787 ◽

Cited By ~ 5

Author(s):

Nikita Basant ◽

Shikha Gupta ◽

Kunwar P. Singh

Keyword(s):

In Silico ◽

Developmental Toxicity ◽

Organic Chemicals ◽

In Silico Prediction ◽

Toxicity Potential

The figure shows performance of the local and global QSAR and ISC-QSAAR models in predicting the developmental toxicity potential of chemicals in rodents.

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