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Quantum Chemical Characterization of Urea Methanolysis: Mechanistic Pathways and Organotin-Catalyzed DMC Formation
(Wiley, 2025)
Martinez‐Arias, Daniel
;
Mora, José R.
;
Rodriguez, Vladimir
;
Marquez, Edgar A.
;
Espinoza‐Montero, Patricio J.
The methanolysis of urea represents a promising green route for the synthesis of dimethyl carbonate (DMC), a versatile compound with applications in sustainable chemistry and energy storage. In this work, a comprehensive quantum chemical investigation of the reaction mechanism is presented using density functional theory (DFT), focusing on both uncatalyzed and organotin-catalyzed systems, considering both stepwise and concerted pathways. For MC production, both the stepwise and the concerted mechanisms mediated by a methanol dimer exhibit the lowest activation enthalpies. Consequently, an effective activation enthalpy of 24.0 kcal/mol was determined, in excellent agreement with the experimental value of 23.45 kcal/mol. In contrast, the bimolecular stepwise and concerted models exhibited higher barriers (ΔH‡ ≈ 42–52 kcal/mol). Entropy values indicated that mechanisms with two methanol molecules involve higher preorganization (ΔS‡ ≈ −60 cal/mol K), compared to −30 cal/mol K in single-molecule pathways. For DMC production from the methyl carbamate intermediate, the rate-limiting step, it was analyzed with and without an organotin catalyst. Catalysis lowers the activation enthalpy by approximately 10 kcal/mol, yielding a value of 24.9 kcal/mol for the methanol monomer catalyzed system, in good agreement with the experimental ΔH‡ of 24.3 kcal/mol. To deepen mechanistic understanding, we employed advanced quantum descriptors including reaction force analysis, reaction electronic flux (REF), and natural bond orbital (NBO) charge evolution. These tools revealed synchronous bond rearrangements and electronic polarization effects that govern transition state stability, mainly by the electronic charges of the carbon atom in the carbonyl group and the amine group in the sense Cδ+—Nδ-. This study provides novel mechanistic insights into the dual role of hydrogen bonding and Lewis acid catalysis in DMC synthesis and demonstrates the utility of quantum chemical tools in elucidating complex reaction pathways, offering a foundation for rational catalyst design. ©The authors ©Wiley.
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Rethinking the applicability domain analysis in QSAR models
(Springer, 2024)
Mora, Jose R.
;
Marquez, Edgar A.
;
Pérez-Pérez, Noel
;
Contreras-Torres, Ernesto
;
Perez-Castillo, Yunierkis
Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in “rational” model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates. © 2024 Springer Nature
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