title: Accurate solubility prediction with error bars for electrolytes: a machine learning approach.
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.
authors.affiliation: Fraunhofer FIRST, Kekuléstrasse 7, 12489 Berlin, Germany. anton@first.fraunhofer.de
authors.forename: Anton
authors.initials: A
authors.lastname: Schwaighofer
authors.forename: Timon
authors.initials: T
authors.lastname: Schroeter
authors.forename: Sebastian
authors.initials: S
authors.lastname: Mika
authors.forename: Julian
authors.initials: J
authors.lastname: Laub
authors.forename: Antonius
authors.initials: A
authors.lastname: ter Laak
authors.forename: Detlev
authors.initials: D
authors.lastname: Sülzle
authors.forename: Ursula
authors.initials: U
authors.lastname: Ganzer
authors.forename: Nikolaus
authors.initials: N
authors.lastname: Heinrich
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
mesh_terms: D003198:Computer Simulation; D004573:Electrolytes; D008956:Models, Chemical; D015394:Molecular Structure; D016571:Neural Networks, Computer; D012995:Solubility
chemical_list: D004573:Electrolytes