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

Activity Relationship ◽

Quantitative Structure ◽

Molecular Image ◽

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

Quantitative structure–activity relationship analysis using deep learning based on a novel molecular image input technique

Bioorganic & Medicinal Chemistry Letters ◽

10.1016/j.bmcl.2018.08.032 ◽

2018 ◽

Vol 28 (20) ◽

pp. 3400-3403 ◽

Cited By ~ 13

Author(s):

Yoshihiro Uesawa

Keyword(s):

Deep Learning ◽

Activity Relationship ◽

Quantitative Structure ◽

Relationship Analysis ◽

Molecular Image ◽

A natural language processing approach based on embedding deep learning from heterogeneous compounds for quantitative structure–activity relationship modeling

Chemical Biology & Drug Design ◽

10.1111/cbdd.13742 ◽

2020 ◽

Vol 96 (3) ◽

pp. 961-972

Author(s):

Khalid Bouhedjar ◽

Abdelbasset Boukelia ◽

Abdelmalek Khorief Nacereddine ◽

Anouar Boucheham ◽

Amine Belaidi ◽

...

Keyword(s):

Deep Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Activity Relationship ◽

Quantitative Structure ◽

Structure Activity ◽

Processing Approach

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

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 ◽

Comprehensive Strategies of Machine Learning Based Quantitative Structure-Activity Relationship Models

iScience ◽

10.1016/j.isci.2021.103052 ◽

2021 ◽

pp. 103052

Author(s):

Jiashun Mao ◽

Javed Akhtar ◽

Xiao Zhang ◽

Liang Sun ◽

Shenghui Guan ◽

...

Keyword(s):

Machine Learning ◽

Activity Relationship ◽

Quantitative Structure ◽

Deep learning meets quantitative structure–activity relationship (QSAR) for leveraging structure-based prediction of solute rejection in organic solvent nanofiltration

Journal of Membrane Science ◽

10.1016/j.memsci.2022.120268 ◽

2022 ◽

pp. 120268

Author(s):

Gergo Ignacz ◽

Gyorgy Szekely

Keyword(s):

Deep Learning ◽

Organic Solvent ◽

Activity Relationship ◽

Quantitative Structure ◽

Structure Activity ◽

Organic Solvent Nanofiltration

Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure–Activity Relationship (QSAR) Analysis

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2019.00065 ◽

2019 ◽

Vol 7 ◽

Cited By ~ 9

Author(s):

Yasunari Matsuzaka ◽

Yoshihiro Uesawa

Keyword(s):

Deep Learning ◽

Activity Relationship ◽

Learning Method ◽

Quantitative Structure ◽

Qsar Analysis ◽

Molecular Image ◽

Structure Activity ◽

Classification Of Images

AutoQSAR: an automated machine learning tool for best-practice quantitative structure–activity relationship modeling

Future Medicinal Chemistry ◽

10.4155/fmc-2016-0093 ◽

2016 ◽

Vol 8 (15) ◽

pp. 1825-1839 ◽

Cited By ~ 29

Author(s):

Steven L Dixon ◽

Jianxin Duan ◽

Ethan Smith ◽

Christopher D Von Bargen ◽

Woody Sherman ◽

...

Keyword(s):

Machine Learning ◽

Best Practice ◽

Activity Relationship ◽

Quantitative Structure ◽

Learning Tool ◽

Structure Activity ◽

Automated Machine Learning ◽

Machine Learning Tool