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