MMH-2 as a new approach for the prediction of intermolecular interactions: the crystal packing of acetamide


Edelsys Codorniu-Hernández,*a A. Daniel Boese,*b Carsten Schauerte,*b Alberto Rolo-Naranjo,*a Ramón Miranda-Quintana,*a Luis A. Montero-Cabrerac and Roland Boese*b


* Corresponding authors

*a Department of Molecular Design and Synthesis, Higher Institute of Technologies and Applied Sciences, Ave Salvador Allende y Luaces, Quinta de los Molinos, Plaza de la Revolución, AP 6163, Ciudad Habana, Cuba

*b Department of Chemistry, University of Duisburg-Essen, Universitätsstrasse 5-7, Essen, Germany

*c Laboratory of Computational and Theoretical Chemistry, Faculty of Chemistry, University of Havana, Zapata e G y Mazón, Ciudad Habana, Cuba



CrystEngComm, 2009,11, 2358-2370

[DOI: 10.1039/B905779J]




A new approach (MMH-2) was applied and tested for the prediction of intermolecular interactions in the crystal packing of acetamide. In MMH-2, energies of random molecular interaction configurations are computed. It uses molecular association quantities from statistical thermodynamics in order to obtain intermolecular interaction motifs that follow a ranking process. The most important motifs are optimized. Here, the AM1 semiempirical Hamiltonian was applied for the calculation and optimization of each obtained configuration and a comparison to MP2 results is provided. Such a stepwise procedure follows the assumed genesis of crystal growth without using experimental input. For evaluation purposes, graph set analysis was used to classify the structural patterns of both acetamide polymorphs. It was also necessary to introduce a new geometrical similarity index for the comparison of calculated and experimental motifs. As a result, all experimental hydrogen bond patterns were found and molecular synthons in both polymorphic acetamide structures were predicted as local minima. This suggests a new strategy for crystal structure prediction of flexible molecules with a possible subsequent progress in crystal engineering in silico.

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