On the novel quantitative structure-activity relationships (QSAR) descriptors .SIGMA.X(X=s0) and .MU.2/.ALPHA. deduced from the modified Lennard-Jones 12,6 potentials.

YOSHIO SASAKI; TATSUYA TAKAGI; HIDEKO KAWAKI

doi:10.1248/cpb.36.3743

On the novel quantitative structure-activity relationships (QSAR) descriptors .SIGMA.X(X=s0) and .MU.2/.ALPHA. deduced from the modified Lennard-Jones 12,6 potentials.

Chemical and Pharmaceutical Bulletin ◽

10.1248/cpb.36.3743 ◽

1988 ◽

Vol 36 (10) ◽

pp. 3743-3752 ◽

Cited By ~ 1

Author(s):

YOSHIO SASAKI ◽

TATSUYA TAKAGI ◽

HIDEKO KAWAKI

Keyword(s):

The Novel ◽

Quantitative Structure ◽

Structure Activity Relationships ◽

Lennard Jones ◽

Structure Activity ◽

Quantitative Structure Activity Relationships

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Quantitative structure–activity relationships (QSARs) using the novel marine algal toxicity data of phenols

Journal of Molecular Graphics and Modelling ◽

10.1016/j.jmgm.2012.06.002 ◽

2012 ◽

Vol 38 ◽

pp. 90-100 ◽

Cited By ~ 25

Author(s):

M. Doğa Ertürk ◽

Melek Türker Saçan ◽

Marjana Novic ◽

Nikola Minovski

Keyword(s):

The Novel ◽

Toxicity Data ◽

Quantitative Structure ◽

Structure Activity Relationships ◽

Structure Activity ◽

Algal Toxicity ◽

Quantitative Structure Activity Relationships ◽

Marine Algal

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Energy aspects of oil/water partition leading to the novel hydrophobic parameters for the analysis of quantitative structure-activity relationships

Journal of Medicinal Chemistry ◽

10.1021/jm00096a014 ◽

1992 ◽

Vol 35 (18) ◽

pp. 3382-3387 ◽

Cited By ~ 22

Author(s):

Yong Zhong Da ◽

Katsuhiko Ito ◽

Hideaki Fujiwara

Keyword(s):

The Novel ◽

Quantitative Structure ◽

Structure Activity Relationships ◽

Structure Activity ◽

Oil Water ◽

Quantitative Structure Activity Relationships ◽

Energy Aspects

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Studies on quantitative structure-activity relationships. IV. Usefulness of the novel substituent entropy constant .SIGMA.s and the sign of the enthalpy parameter.

Chemical and Pharmaceutical Bulletin ◽

10.1248/cpb.30.3069 ◽

1982 ◽

Vol 30 (9) ◽

pp. 3069-3073

Author(s):

YOSHIO SASAKI ◽

TATSUYA TAKAGI ◽

AKIHIRO IWATA ◽

HIDEKO KAWAKI

Keyword(s):

The Novel ◽

Quantitative Structure ◽

Structure Activity Relationships ◽

Structure Activity ◽

Quantitative Structure Activity Relationships

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Determination of the novel quantitative structure-activity relationships descriptor .SIGMA.S.DEG. for di- and trisubstituted benzene derivatives.

Chemical and Pharmaceutical Bulletin ◽

10.1248/cpb.37.3268 ◽

1989 ◽

Vol 37 (12) ◽

pp. 3268-3271 ◽

Cited By ~ 4

Author(s):

Hideko KAWAKI ◽

Yoshio SASAKI ◽

Tatsuya TAKAGI ◽

Shiho FUJII ◽

Fumika MASUDA

Keyword(s):

The Novel ◽

Benzene Derivatives ◽

Quantitative Structure ◽

Structure Activity Relationships ◽

Structure Activity ◽

Quantitative Structure Activity Relationships

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Quantitative Structure-Activity Relationships of Potential Antimalarial Drugs Active Against Chloroquine-Resistant Plasmodium Falciparum

Planta Medica ◽

10.1055/s-2008-1075144 ◽

2008 ◽

Vol 74 (03) ◽

Author(s):

SJ Hocart ◽

H Liu ◽

D De ◽

FM Krogstad ◽

DJ Krogstad

Keyword(s):

Plasmodium Falciparum ◽

Antimalarial Drugs ◽

Quantitative Structure ◽

Structure Activity Relationships ◽

Structure Activity ◽

Quantitative Structure Activity Relationships

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Parabolic Quantitative Structure–Activity Relationships and Photodynamic Therapy: Application of a Three-Compartment Model with Clearance to the In Vivo Quantitative Structure–Activity Relationships of a Congeneric Series of Pyropheophorbide Derivatives Used as Photosensitizers for Photodynamic Therapy

Photochemistry and Photobiology ◽

10.1562/0031-8655(1999)070<0781:pqsrap>2.3.co;2 ◽

1999 ◽

Vol 70 (5) ◽

pp. 781 ◽

Cited By ~ 2

Author(s):

W. R. Potter ◽

B. W. Henderson ◽

D. A. Bellnier ◽

R. K. Pandey ◽

L. A. Vaughan ◽

...

Keyword(s):

Photodynamic Therapy ◽

Compartment Model ◽

Quantitative Structure ◽

Structure Activity Relationships ◽

Structure Activity ◽

Three Compartment Model ◽

Quantitative Structure Activity Relationships

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Binding Affinity of Triphenyl Acrylonitriles to Estrogen Receptors: Quantitative Structure-Activity Relationships

Folia Medica ◽

10.2478/v10153-010-0005-2 ◽

2010 ◽

Vol 52 (3) ◽

Cited By ~ 1

Author(s):

Sorana Bolboacă ◽

Monica Marta ◽

Lorentz Jäntschi

Keyword(s):

Estrogen Receptors ◽

Binding Affinity ◽

Quantitative Structure ◽

Structure Activity Relationships ◽

Structure Activity ◽

Quantitative Structure Activity Relationships

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Quantitative Structure-Activity Relationships for Cellular Uptake of Surface-Modified Nanoparticles

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207318666150306105525 ◽

2015 ◽

Vol 18 (4) ◽

pp. 365-375 ◽

Cited By ~ 6

Author(s):

Rong Liu ◽

Robert Rallo ◽

Muhammad Bilal ◽

Yoram Cohen

Keyword(s):

Cellular Uptake ◽

Quantitative Structure ◽

Structure Activity Relationships ◽

Structure Activity ◽

Quantitative Structure Activity Relationships ◽

Surface Modified ◽

Surface Modified Nanoparticles

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Investigation of the Structural Requirement for Blocking the Human CCR5 Chemokine Receptor. An Insight from Quantitative Structure Activity Relationships Study

Letters in Drug Design & Discovery ◽

10.2174/157018009787582615 ◽

2009 ◽

Vol 6 (2) ◽

pp. 114-124 ◽

Cited By ~ 1

Author(s):

Nigus Dessalew

Keyword(s):

Chemokine Receptor ◽

Quantitative Structure ◽

Structural Requirement ◽

Ccr5 Chemokine Receptor ◽

Structure Activity Relationships ◽

Structure Activity ◽

Quantitative Structure Activity Relationships ◽

Human Ccr5

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Quantitative structure activity relationships (QSARs) and machine learning models for abiotic reduction of organic compounds by an aqueous Fe(II) complex

Water Research ◽

10.1016/j.watres.2021.116843 ◽

2021 ◽

Vol 192 ◽

pp. 116843

Author(s):

Yidan Gao ◽

Shifa Zhong ◽

Tifany L. Torralba-Sanchez ◽

Paul G. Tratnyek ◽

Eric J. Weber ◽

...

Keyword(s):

Machine Learning ◽

Organic Compounds ◽

Quantitative Structure ◽

Learning Models ◽

Structure Activity Relationships ◽

Structure Activity ◽

Quantitative Structure Activity Relationships ◽

Abiotic Reduction ◽

Machine Learning Models

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