A new approach to radial basis function approximation and its application to QSAR. - Wikidata - awgldk - TriplyDB

A new approach to radial basis function approximation and its application to QSAR.

A new approach to radial basis function approximation and its application to QSAR.

http://www.wikidata.org/entity/Q37701459

about

Many InChIs and quite some feat CERAPP: Collaborative Estrogen Receptor Activity Prediction Project Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides?

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P2860

A new approach to radial basis function approximation and its application to QSAR.

http://www.wikidata.org/entity/Q37701459

description

article científic

@ca

article scientifique

@fr

articolo scientifico

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artigo científico

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bilimsel makale

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scientific article published on 28 February 2014

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vedecký článok

@sk

vetenskaplig artikel

@sv

videnskabelig artikel

@da

vědecký článek

@cs

name

A new approach to radial basis function approximation and its application to QSAR.

@en

A new approach to radial basis function approximation and its application to QSAR.

@nl

type

label

A new approach to radial basis function approximation and its application to QSAR.

@en

A new approach to radial basis function approximation and its application to QSAR.

@nl

prefLabel

A new approach to radial basis function approximation and its application to QSAR.

@en

A new approach to radial basis function approximation and its application to QSAR.

@nl

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P1433

Journal of Chemical Information and Modeling

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A new approach to radial basis function approximation and its application to QSAR

@en

P2093

Alexey V Zakharov

http://www.w3.org/2001/XMLSchema#string

Markus Sitzmann

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Megan L Peach

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P2860

Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure

Computational tools and resources for metabolism-related property predictions. 2. Application to prediction of half-life time in human liver microsomes

Quantitative prediction of antitarget interaction profiles for chemical compounds.

Data modelling with neural networks: advantages and limitations.

Neural networks in building QSAR models.

In silico physicochemical parameter predictions.

QNA-based 'Star Track' QSAR approach.

Prediction of noninteractive mixture toxicity of organic compounds based on a fuzzy set method.

Synthesis, antifungal activity and QSAR study of 2-arylhydroxynitroindoles.

Importance of physicochemical properties for the design of new pesticides.

P304

713-719

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scholarly article

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10.1021/CI400704F

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3

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54

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Marc C Nicklaus

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2014-02-28T00:00:00Z

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259875787

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24451033

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3985791

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