PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach
Anton Schwaighofer, Timon Schroeter, Sebastian Mika, Julian Laub, Antonius ter Laak, Detlev Suelzle, Ursula Ganzer, Nikolaus Heinrich and Klaus-Robert Müller
Journal of Chemical Information and Modeling Volume 47, Number 2, pp. 407-424, 2007.

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 modelling 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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:2504
Deposited By:Anton Schwaighofer
Deposited On:22 November 2006