(Q56916371)

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Accurate In Silico log PPredictions: One Can't Embrace the Unembraceable

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title

Accurate In Silico log PPredictions: One Can't Embrace the Unembraceable (English)

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author name string

Igor V. Tetko

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1

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Gennadiy I. Poda

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2

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Claude Ostermann

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3

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Raimund Mannhold

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4

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publication date

August 2009

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published in

QSAR and Combinatorial Science

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volume

28

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page(s)

845-849

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issue

8

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cites work

Cheminformatics Analysis of Organic Substituents: Identification of the Most Common Substituents, Calculation of Substituent Properties, and Automatic Identification of Drug-like Bioisosteric Groups

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https://api.crossref.org/works/10.1002%2FQSAR.200960003

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21 January 2018

Neural network studies. 4. Introduction to associative neural networks.

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21 January 2018

Application of Associative Neural Networks for Prediction of Lipophilicity in ALOGPS 2.1 Program

1 reference

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Crossref

reference URL

https://api.crossref.org/works/10.1002%2FQSAR.200960003

retrieved

21 January 2018

Virtual Computational Chemistry Laboratory – Design and Description

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4 April 2022

https://opencitations.net/index/coci/api/v1/citations/10.1007/S10822-005-8694-Y

Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52

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COCI

reference URL

https://opencitations.net/index/api/v1/citations/10.1177/026119290503300209

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18 January 2024

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10.1002/QSAR.200960003

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(Q56916371)

Accurate In Silico log PPredictions: One Can't Embrace the Unembraceable

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