Sammanfattning

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.

Publicerad i

Journal Of Chemical Information And Computer Sciences
2003, Volym: 43, nummer: 6, sidor: 1855–1859

UKÄ forskningsämne

Farmaceutisk synteskemi
Datavetenskap (datalogi)


Publikationens identifierare

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

Permanent länk till denna sida (URI)

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