Abstract
A priori calculation of thermophysical properties and predictive thermodynamic models can be very helpful for developing new industrial processes. Group contribution methods link the target property to contributions based on chemical groups or other molecular subunits of a given molecule. However, the fragmentation of the molecule into its subunits is usually done manually impeding the fast testing and development of new group contribution methods based on large databases of molecules. The aim of this work is to develop strategies to overcome the challenges that arise when attempting to fragment molecules automatically while keeping the definition of the groups as simple as possible. Furthermore, these strategies are implemented in two fragmentation algorithms. The first algorithm finds only one solution while the second algorithm finds all possible fragmentations. Both algorithms are tested to fragment a database of 20,000+ molecules for use with the group contribution model Universal Quasichemical Functional Group Activity Coefficients (UNIFAC). Comparison of the results with a reference database shows that both algorithms are capable of successfully fragmenting all the molecules automatically. Furthermore, when applying them on a larger database it is shown, that the newly developed algorithms are capable of fragmenting structures previously thought not possible to fragment.
Highlights
Cheminformatics is a growing field due to the increasing computational capabilities and improvements in the accuracy achieved by its predictions
Group contribution models have been successfully applied to a wide variety of properties including density [1, 2], critical properties [3–5], enthalpy of vaporization [6], normal boiling points [7, 8], water–octanol partition coefficients [9–11], infinite dilution activity coefficients [12] and many more
The focus of this work is to develop a fragmentation algorithm that is as independent as possible from the chosen fragmentation scheme to allow for a faster development of new group contribution methods
Summary
Cheminformatics is a growing field due to the increasing computational capabilities and improvements in the accuracy achieved by its predictions. The chemical space is vast and the number of molecules available to produce with new and, in some cases even automated synthetizing routes increases. For the application of thermodynamic models or a priori calculation of thermophysical properties, predictive methods can be helpful and in some cases even necessary. These methods, which relate properties to the molecule structures are usually. One subgroup of these models is the group contribution method. The idea behind this method is to divide the value of a property of the complete molecule into its contributions based on the chemical groups or other molecular subunit.
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