Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach

Anton Schwaighofer, Timon Schroeter, Sebastian Mika, Julian Laub, Antonius ter Laak, Detlev Sülzle, Ursula Ganzer, Nikolaus Heinrich, and Klaus-Robert Müller

Abstract

Accurate in silico models for predicting aqueous solubility are needed in drug design and discovery and many other areas of chemical research. We present a statistical modeling of aqueous solubility based on measured data, using a Gaussian Process nonlinear regression model (GPsol). We compare our results with those of 14 scientific studies and 6 commercial tools. This shows that the developed model achieves much higher accuracy than available commercial tools for the prediction of solubility of electrolytes. On top of the high accuracy, the proposed machine learning model also provides error bars for each individual prediction.

Details

Publication typeArticle
Published inJournal of Chemical Information and Modelling
URLhttp://dx.doi.org/10.1021/ci600205g
Pages407–424
Volume47
Number2
> Publications > Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach