Nonlinear prediction of quantitative structure-activity relationships

Peter Tiño, Ian T. Nabney, Bruce S. Williams, Jens Lösel, Yi Sun

Research output: Contribution to journalArticle (Academic Journal)peer-review

19 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)1647-1653
Number of pages7
JournalJournal of Chemical Information and Computer Sciences
Volume44
Issue number5
DOIs
Publication statusPublished - 1 Sep 2004

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