Nonlinear Prediction of Quantitative Structure−Activity Relationships
- 28 July 2004
- journal article
- research article
- Published by American Chemical Society (ACS) in Journal of Chemical Information and Computer Sciences
- Vol. 44 (5) , 1647-1653
- https://doi.org/10.1021/ci034255i
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.Keywords
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