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, Klaus Robert Müller

Research output: Contribution to journalArticle

55 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)407-424
Number of pages18
JournalJournal of Chemical Information and Modeling
Volume47
Issue number2
DOIs
Publication statusPublished - 2007 Mar

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Computer Science Applications
  • Library and Information Sciences

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