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.
|Number of pages||7|
|Journal||Journal of Chemical Information and Computer Sciences|
|Publication status||Published - 1 Sep 2004|