Predicting the log of the partition coefficient P is a long-standing benchmark problem in QuantitativeStructure−Activity Relationships (QSAR). In this paper we show that a relatively simple molecularrepresentation (using 14 variables) can be combined with leading edge machine learning algorithms to predictlogP on new compounds more accurately than existing benchmark algorithms which use complex molecularrepresentations.
