scholarly journals Generating accurate in silico predictions of acute aquatic toxicity for a range of organic chemicals: Towards similarity-based machine learning methods

Chemosphere ◽  
2021 ◽  
pp. 130681
Author(s):  
Agnieszka Gajewicz-Skretna ◽  
Ayako Furuhama ◽  
Hiroshi Yamamoto ◽  
Noriyuki Suzuki
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
Aquatic Toxicity ◽  
Organic Chemicals ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Acute Aquatic Toxicity ◽  
In Silico Predictions

In silicoestimation of chemical aquatic toxicity on crustaceans using chemical category methods

2018 ◽  
Vol 20 (9) ◽  
pp. 1234-1243 ◽  
Author(s):  
Qianqian Cao ◽  
Lin Liu ◽  
Hongbin Yang ◽  
Yingchun Cai ◽  
Weihua Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
Aquatic Toxicity ◽  
Learning Methods ◽  
Molecular Fingerprints ◽  
Machine Learning Methods ◽  
Acute Aquatic Toxicity

A series ofin silicomodels were developed to estimate chemical acute aquatic toxicity on crustaceans using machine learning methods combined with molecular fingerprints.

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.

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.

In Silico Machine Learning Methods in Drug Development

2014 ◽  
Vol 14 (16) ◽  
pp. 1913-1922 ◽  
Author(s):  
Dimitar Dobchev ◽  
Girinath Pillai ◽  
Mati Karelson
Keyword(s):  
Machine Learning ◽  
Drug Development ◽  
In Silico ◽  
Learning Methods ◽  
Machine Learning Methods

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 Assessment of predictive models for estimating the acute aquatic toxicity of organic chemicals

2016 ◽  
Vol 18 (16) ◽  
pp. 4432-4445 ◽  
Author(s):  
Fjodor Melnikov ◽  
Jakub Kostal ◽  
Adelina Voutchkova-Kostal ◽  
Julie B. Zimmerman ◽  
Paul T. Anastas
Keyword(s):  
Predictive Models ◽  
In Silico ◽  
Aquatic Toxicity ◽  
Organic Chemicals ◽  
Toxicity Data ◽  
Acute Aquatic Toxicity ◽  
Data Gaps

In silico toxicity models are critical in addressing experimental aquatic toxicity data gaps and prioritizing chemicals for further assessment.

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.

Sign in / Sign up

Export Citation Format

Share Document