Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine
Anton Schwaighofer, Timon Schroeter, Sebastian Mika, Antonius ter Laak, Detlev Suelzle and Nikolaus Heinrich
In: 2nd German Conference on Chemoinformatics / 20th CIC Workshop, 12. - 14. November 2006, Goslar, Germany.
Accurate in-silico models for predicting aqueous solubility are needed in
drug design and discovery, and many other areas of chemical research. A
first principles modelling of solubility, however, would be overly
complex, since too many physical factors with separate mechanisms are
involved in the phase transition from solid to solvated molecules.
We present a machine learning approach (Gaussian Process model) that
provides a statistical modeling of aqueous solubility based on measured
data. The model was validated on the well known set of 1311 compounds by
Huuskonen et.al., and on an in-house dataset of 632 drug candidates at
We compare our results with those of 14 scientific studies and 6
commercial tools. For 91\% of the Huuskonen compounds, our predictions
were correct within one order of magnitude, even though the respective
compounds were not used in training the model. Existing commercial
software achieves 79\% correct predictions within one order of magnitude.
On the 632 drug candidates (mostly electrolytes), 82\% of our predictions
are correct within one order of magnitude, compared to only 64\% achieved
by commercial software. Additional validations with new in-house measured
data will be presented as well. On top of the accurate predictions, the
proposed machine learning model also provides confidence estimates for
each individual prediction.
|EPrint Type:||Conference or Workshop Item (Oral)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Theory & Algorithms|
|Deposited By:||Anton Schwaighofer|
|Deposited On:||22 November 2006|