Molecular Graph Modularity as a Descriptor for Property EstimationApplication to the Viscosity of Biomass-Derived Molecules

The diversity of biomass and its conversion processes produce a diverse pool of functional molecules. For many of these molecules, property data have not been measured yet and need to be estimated to determine suitability to a particular application. For example, viscosity is a key property in the development of green solvents, fuel additives, and biofuels. This paper proposes the use of modularity as a molecular descriptor combined with functional group counts to estimate molecular properties using neural network models. The modularity of molecules was determined from graph representations using community detection algorithms. The potential for this approach was demonstrated for the modeling of viscosity at 25 °C and applied to biomass-derived molecules. The model performances showed that including modularity contributed to more accurate estimations than viscosity models existing in the literature. Furthermore, modularity on its own can be useful to estimate viscosity for n-alkanes, esters, isoalkanes, aldehydes, aromatics, and cycloalkanes. This was due to the capacity of modularity to capture the structural features of the molecules employed in the data set. As such, modularity exhibited its tremendous potential to be exploited for property estimation, supporting the screening, rational design, and engineering of green chemicals derived from biomass.