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

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• 作者：Anton Schwaighofer ; Timon Schroeter ; Sebastian Mika ; Julian Laub ; Antonius ter Laak ; Detlev Sü ; lzle ; Ursula Ganzer ; Nikolaus Heinrich ; Klaus-Robert Mü ; ller
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2007
• 出版时间：March 2007
• 年：2007
• 卷：47
• 期：2
• 页码：407 - 424
• 全文大小：587K
• 年卷期：v.47,no.2(March 2007)
• ISSN：1549-960X

文摘

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