Several standard semiempirical methods as well as the MMFF94 force field approximation have been tested in reproducing 8 DLPNO-CCSD(T)/cc-pVTZ level conformational energies and spatial structures for 37 organic molecules representing pharmaceuticals, drugs, catalysts, synthetic precursors, industry-related chemicals (37conf8 database). All contemporary semiempirical methods surpass their standard counterparts resulting in more reliable conformational energies and spatial structures, even though at significantly higher computational costs. However, even these methods show unexpected failures in reproducing energy differences between several conformers of the crown ether 1,4,7,10,13,16-hexaoxacyclooctadecane (18-crown-6). Inexpensive force field MMFF94 approximation groups with contemporary semiempirical methods in reproducing the correct order of conformational energies and spatial structures, although the performance in predicting absolute conformational energies compares to standard semiempirical methods. Based on these findings, we suggest a two-step strategy for reliable yet feasible conformational search and sampling in realistic-size flexible organic molecules: i) geometry optimization/preselection of relevant conformers using the MMFF94 force field; ii) single-point energy evaluations using a contemporary semiempirical method. We expect that developed database 37conf8 is going to be useful for development of semiempirical methods.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The research reported in this publication was supported by funding from the Government of the Russian Federation (Agreement № 074-02-2018-286). L.C. gratefully acknowledges the financial support from King Abdullah University of Science and Technology (KAUST). A. G. gratefully acknowledges support from the RFBR Grant № 17-03-00564. For computer time, this research used the resources of the Supercomputing Laboratory at King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia and Novosibirsk State University Supercomputer Center (NUSC) in Novosibirsk, the Russian Federation.