SurfPro - A curated database and predictive model of experimental properties of surfactants

Dataset and code used for the publication in Digital Discovery titled
'SurfPro - A curated database and predictive model of experimental properties of surfactants'.

See also https://github.com/BigChemistry-RobotLab/SurfPro

Abstract:
Despite great industrial interest, modeling the physical properties of surfactants in water based
on their molecular structure remains a challenge. A significant part of this challenge is in obtaining
sufficient amounts of high-quality data. Experimentally determined properties such the critical micelle
concentration (CMC) and surface tension at CMC (γCMC) have been reported for many surfactants.
However, surfactant data are scattered across many literature sources, and reported in a manner
which is often unsuitable as input for predictive models. In this work, we address this limitation by
compiling the SurfPro database of Surfactant Properties. SurfPro consists of 1624 surfactant entries
curated from 223 literature sources, containing 1395 CMC values, 972 γCMC values and more than
657 values for Γmax, C20, πCMC and Amin. However, only 647 structures have all reported properties,
and for most surfactants multiple properties are missing. We trained a previously reported graph
neural network architecture for single- and multi-property prediction on these incomplete data of all
surfactant types in the database to accurately predict pCMC (− log10(CMC)), γCMC, Γmax and pC20.
We achieved state-of-the-art performance of these four properties using an ensemble of AttentiveFP
models trained on ten different folds of the training data in the multi-property setting. Finally, we
leveraged the predictions and uncertainties of the ensemble model to impute all missing properties
for all 977 surfactants with an incomplete set of properties. We make our curated SurfPro database,
proposed test split and training datasets, the imputed database, as well as our code publicly available.