PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Non-linear prediction of Quantitative Structure-Activity Relationships
P tino, Ian Nabney, BS Williams and J Loesel
Journal of Chemical Informatics and Computer Science Volume 44, pp. 1647-1653, 2004.

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:906
Deposited By:Dharmesh Maniyar
Deposited On:06 January 2005