Binary Classification of a Large Collection of Environmental Chemicals from Estrogen Receptor Assays by Quantitative Structure–Activity Relationship and Machine Learning Methods

Environmental Chemicals ◽

Quantitative Structure ◽

Large Collection ◽

Structure Activity ◽

Receptor Assays

Quantitative structure-activity relationship (QSAR) models and their applicability domain analysis on HIV-1 protease inhibitors by machine learning methods

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2019.103888 ◽

2020 ◽

Vol 196 ◽

pp. 103888

Author(s):

Yujia Tian ◽

Shengde Zhang ◽

Hongyan Yin ◽

Aixia Yan

Keyword(s):

Machine Learning ◽

Protease Inhibitors ◽

Domain Analysis ◽

Applicability Domain ◽

Quantitative Structure ◽

Structure Activity ◽

Qsar Models ◽

Hiv 1

Quantitative Structure-Activity Relationship Study of the Non-Nucleoside Inhibitors of HCV NS5B Polymerase by Machine Learning Methods

Acta Physico-Chimica Sinica ◽

10.3866/pku.whxb201305171 ◽

2013 ◽

Vol 29 (08) ◽

pp. 1639-1647

Author(s):

CONG Yong ◽

◽

XUE Ying ◽

Keyword(s):

Machine Learning ◽

Quantitative Structure ◽

Learning Methods ◽

Structure Activity ◽

Relationship Study ◽

Hcv Ns5b ◽

Nucleoside Inhibitors ◽

Ns5b Polymerase

Advances in Intelligent Systems and Computing - Lecture Notes in Computational Intelligence and Decision Making ◽

Binary Classification of Fractal Time Series by Machine Learning Methods

10.1007/978-3-030-26474-1_49 ◽

2019 ◽

pp. 701-711 ◽

Cited By ~ 12

Author(s):

Lyudmyla Kirichenko ◽

Tamara Radivilova ◽

Vitalii Bulakh

Keyword(s):

Machine Learning ◽

Time Series ◽

Binary Classification ◽

Learning Methods ◽

Fractal Time

Quantitative Structure–Activity Relationship Machine Learning Models and their Applications for Identifying Viral 3CLpro- and RdRp-Targeting Compounds as Potential Therapeutics for COVID-19 and Related Viral Infections

ACS Omega ◽

10.1021/acsomega.0c03682 ◽

2020 ◽

Vol 5 (42) ◽

pp. 27344-27358

Author(s):

Julian Ivanov ◽

Dmitrii Polshakov ◽

Junko Kato-Weinstein ◽

Qiongqiong Zhou ◽

Yingzhu Li ◽

...

Keyword(s):

Machine Learning ◽

Viral Infections ◽

Activity Relationship ◽

Quantitative Structure ◽

Learning Models ◽

Structure Activity ◽

Potential Therapeutics ◽

Machine Learning Models

A ternary classification using machine learning methods of distinct estrogen receptor activities within a large collection of environmental chemicals

The Science of The Total Environment ◽

10.1016/j.scitotenv.2016.12.088 ◽

2017 ◽

Vol 580 ◽

pp. 1268-1275 ◽

Cited By ~ 8

Author(s):

Quan Zhang ◽

Lu Yan ◽

Yan Wu ◽

Li Ji ◽

Yuanchen Chen ◽

...

Keyword(s):

Machine Learning ◽

Estrogen Receptor ◽

Environmental Chemicals ◽

Large Collection ◽

Learning Methods ◽

Machine Learning Methods

A Molecular Image-Based Novel Quantitative Structure-Activity Relationship Approach, Deepsnap-Deep Learning and Machine Learning

Current Issues in Molecular Biology ◽

10.21775/cimb.042.455 ◽

2022 ◽

pp. 455-472

Author(s):

Yasunari Matsuzaka ◽

Yoshihiro Uesawa

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Activity Relationship ◽

Quantitative Structure ◽

Molecular Image ◽

Quantitative Structure-Activity Relationship Models for Predicting Drug-Induced Liver Injury Based on FDA-Approved Drug Labeling Annotation and Using a Large Collection of Drugs

Toxicological Sciences ◽

10.1093/toxsci/kft189 ◽

2013 ◽

Vol 136 (1) ◽

pp. 242-249 ◽

Cited By ~ 54

Author(s):

Minjun Chen ◽

Huixiao Hong ◽

Hong Fang ◽

Reagan Kelly ◽

Guangxu Zhou ◽

...

Keyword(s):

Liver Injury ◽

Activity Relationship ◽

Quantitative Structure ◽

Large Collection ◽

Drug Induced ◽

Drug Induced Liver Injury ◽

Drug Labeling ◽

Prediction Model of Clearance by a Novel Quantitative Structure–Activity Relationship Approach, Combination DeepSnap-Deep Learning and Conventional Machine Learning

ACS Omega ◽

10.1021/acsomega.1c03689 ◽

2021 ◽

Vol 6 (36) ◽

pp. 23570-23577

Author(s):

Hideaki Mamada ◽

Yukihiro Nomura ◽

Yoshihiro Uesawa

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Prediction Model ◽

Activity Relationship ◽

Quantitative Structure ◽

Structure Activity ◽

Conventional Machine

Dissecting Machine-Learning Prediction of Molecular Activity: Is an Applicability Domain Needed for Quantitative Structure–Activity Relationship Models Based on Deep Neural Networks?

Journal of Chemical Information and Modeling ◽

10.1021/acs.jcim.8b00348 ◽

2018 ◽

Vol 59 (1) ◽

pp. 117-126 ◽

Cited By ~ 17

Author(s):

Ruifeng Liu ◽

Hao Wang ◽

Kyle P. Glover ◽

Michael G. Feasel ◽

Anders Wallqvist

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Neural Networks ◽

Applicability Domain ◽

Activity Relationship ◽

Quantitative Structure ◽

Quantitative structure-activity relationship and machine learning studies of 2-thiazolylhydrazone derivatives with anti-Cryptococcus neoformans activity

Journal of Biomolecular Structure and Dynamics ◽

10.1080/07391102.2021.1935321 ◽

2021 ◽

pp. 1-12

Author(s):

Philipe de Oliveira Fernandes ◽

João Paulo A. Martins ◽

Eduardo B. de Melo ◽

Renata Barbosa de Oliveira ◽

Thales Kronenberger ◽

...

Keyword(s):

Machine Learning ◽

Cryptococcus Neoformans ◽

Activity Relationship ◽

Quantitative Structure ◽