Abstract

Support Vector Machines (SVMs) are used to estimate aqueous solubility of organic compounds. A SVM equipped with a Tanimoto similarity kernel estimates solubility with accuracy comparable to results from other reported methods where the same data sets have been studied. Complete cross-validation on a diverse data set resulted in a root-mean-squared error = 0.62 and R² = 0.88. The data input to the machine is in the form of molecular fingerprints. No physical parameters are explicitly involved in calculations.

Published in

Journal Of Chemical Information And Computer Sciences
2003, Volume: 43, number: 6, pages: 1855–1859

UKÄ Subject classification

Pharmaceutical Chemistry
Computer Science


Publication identifier

DOI: https://doi.org/10.1021/ci034107s

Permanent link to this page (URI)

https://res.slu.se/id/publ/108052