Heat of Formation Assessment of Organic Azido Compounds Used as Green Energetic Plasticizers by QSPR Approaches

Sheibani, Nasser

doi:10.1002/prep.201900082

Cited by 5 publications

(1 citation statement)

References 38 publications

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“…Finally, this study was not constrained to molecules belonging to a limited number of chemical families and structures, but, within the perspective of the discovery of new molecules for various applications, the development of models that will be applicable to a large diversity of molecules was pursued. Note, that this is a specific differentiating point of the present study and a major challenge as many reported studies are restricted to molecules of specific chemical families and/or structural characteristics [51][52][53][54][55][56][57].…”

Section: Introductionmentioning

confidence: 99%

On the Development of Descriptor-Based Machine Learning Models for Thermodynamic Properties: Part 1—From Data Collection to Model Construction: Understanding of the Methods and Their Effects

Trinh,

Tbatou,

Lasala

et al. 2023

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In the present work, a multi-angle approach is adopted to develop two ML-QSPR models for the prediction of the enthalpy of formation and the entropy of molecules, in their ideal gas state. The molecules were represented by high-dimensional vectors of structural and physico-chemical characteristics (i.e., descriptors). In this sense, an overview is provided of the possible methods that can be employed at each step of the ML-QSPR procedure (i.e., data preprocessing, dimensionality reduction and model construction) and an attempt is made to increase the understanding of the effects related to a given choice or method on the model performance, interpretability and applicability domain. At the same time, the well-known OECD principles for the validation of (Q)SAR models are also considered and addressed. The employed data set is a good representation of two common problems in ML-QSPR modeling, namely the high-dimensional descriptor-based representation and the high chemical diversity of the molecules. This diversity effectively impacts the subsequent applicability of the developed models to a new molecule. The data set complexity is addressed through customized data preprocessing techniques and genetic algorithms. The former improves the data quality while limiting the loss of information, while the latter allows for the automatic identification of the most important descriptors, in accordance with a physical interpretation. The best performances are obtained with Lasso linear models (MAE test = 25.2 kJ/mol for the enthalpy and 17.9 J/mol/K for the entropy). Finally, the overall developed procedure is also tested on various enthalpy and entropy related data sets from the literature to check its applicability to other problems and competing performances are obtained, highlighting that different methods and molecular representations can lead to good performances.

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