Nonlinear Prediction of Quantitative Structure−Activity Relationships

Predicting the log of the partition coefficient P is a long-standing benchmark problem in Quantitative Structure−Activity Relationships (QSAR). In this paper we show that a relatively simple molecular representation (using 14 variables) can be combined with leading edge machine learning algorithms t...

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Bibliographic Details
Published inJournal of Chemical Information and Computer Sciences Vol. 44; no. 5; pp. 1647 - 1653
Main Authors Tiño, Peter, Nabney, Ian T, Williams, Bruce S, Lösel, Jens, Sun, Yi
Format Journal Article
LanguageEnglish
Published United States American Chemical Society 01.09.2004
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Summary:Predicting the log of the partition coefficient P is a long-standing benchmark problem in Quantitative Structure−Activity Relationships (QSAR). In this paper we show that a relatively simple molecular representation (using 14 variables) can be combined with leading edge machine learning algorithms to predict logP on new compounds more accurately than existing benchmark algorithms which use complex molecular representations.
Bibliography:ark:/67375/TPS-1TMZ630B-K
istex:5E718F590E99C1A13C0769987C4F7D0A40593306
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0095-2338
1549-960X
DOI:10.1021/ci034255i