Fast Prediction of the Equivalent Alkane Carbon Number Using Graph Machines and Neural Networks

The hydrophobicity of oils is a key parameter to design
surfactant/oil/water
(SOW) macro-, micro-, or nano-dispersed systems with the desired features.
This essential physicochemical characteristic is quantitatively expressed
by the equivalent alkane carbon number (EACN) whose experimental determination
is tedious since it requires knowledge of the phase behavior of the
SOW systems at different temperatures and for different surfactant
concentrations. In this work, two mathematical models are proposed
for the rapid prediction of the EACN of oils. They have been designed
using artificial intelligence (machine-learning) methods, namely,
neural networks (NN) and graph machines (GM). While the GM model is
implemented from the SMILES codes of a 111-molecule training set of
known EACN values, the NN model is fed with some σ-moment descriptors
computed with the COSMOtherm software for the 111-molecule set. In
a preliminary step, the leave-one-out algorithm is used to select,
given the available data, the appropriate complexity of the two models.
A comparison of the EACNs of liquids of a fresh set of 10 complex
cosmetic and perfumery molecules shows that the two approaches provide
comparable results in terms of accuracy and reliability. Finally,
the NN and GM models are applied to nine series of homologous compounds,
for which the GM model results are in better agreement with the experimental
EACN trends than the NN model predictions. The results obtained by
the GMs and by the NN based on σ-moments can be duplicated with
the demonstration tool available for download as detailed in the Supporting
Information.