Bulletin of the Korean Chemical Society

Korean Chemical Society (대한화학회)
권호 표지
DOI QR코드

DOI QR Code

Prediction of Physicochemical Properties of Organic Molecules Using Semi-Empirical Methods

  • Received : 2012.11.27
  • Accepted : 2013.01.08
  • Published : 2013.04.20
Download PDF
⟨ Previous Next ⟩

Abstract

Prediction of physicochemical properties of organic molecules is an important process in chemistry and chemical engineering. The MSEP approach developed in our lab calculates the molecular surface electrostatic potential (ESP) on van der Waals (vdW) surfaces of molecules. This approach includes geometry optimization and frequency calculation using hybrid density functional theory, B3LYP, at the 6-31G(d) basis set to find minima on the potential energy surface, and is known to give satisfactory QSPR results for various properties of organic molecules. However, this MSEP method is not applicable to screen large database because geometry optimization and frequency calculation require considerable computing time. To develop a fast but yet reliable approach, we have re-examined our previous work on organic molecules using two semi-empirical methods, AM1 and PM3. This new approach can be an efficient protocol in designing new molecules with improved properties.

Keywords

References

  1. Jurs, P. C. In Encyclopedia of Computational Chemistry; Vol. 4, Schleyer, P. v. R., Ed.; John Wiley & Sons: 1998; p 2320.
  2. Karelson, M. In Molecular Descriptors in QSAR/QSPR; John Wiley & Sons: New York, 2000.
  3. Politzer, P.; Murray, J. S. In Reviews in Computational Chemistry; Vol. 2, Lipkowitz, K. B., Boyd, D. B., Eds.; VCH Publishing: New York, 1991; p 273.
  4. Bader, R. F. W.; Carroll, M. T.; Cheeseman, J. R.; Chang, C. J. Am. Chem. Soc. 1987, 109, 7968. https://doi.org/10.1021/ja00260a006
  5. Bader, R. F. W.; Henneker, W. H.; Cade, P. E. J. Chem. Phys. 1967, 46, 3341. https://doi.org/10.1063/1.1841222
  6. Politzer, P.; Murray, J. S. In Quantitative Treatments of Solute/ Solvent Interactions; Elsevier Amsterdam: 1994; p 243.
  7. Kim, C. K.; Lee, K. A; Hyun, K. H.; Park, H. J.; Kwack, I. Y.; Kim, C. K.; Lee, H. W.; Lee, B.-S. J. Comput. Chem. 2004, 25, 2073. https://doi.org/10.1002/jcc.20129
  8. Kim, C. K.; Cho, S. G.; Kim, C. K.; Park, H.-Y.; Zhang, H.; Lee, H. W. J. Comput. Chem. 2008, 29, 1818. https://doi.org/10.1002/jcc.20943
  9. Kim, C. K.; Cho, S. G.; Li, J.; Kim, C. K.; Lee, H. W. Bull. Korean Chem. Soc. 2011, 32, 4341. https://doi.org/10.5012/bkcs.2011.32.12.4341
  10. Curtiss, L. A.; Raghavachari, K.; Redfern, P. C.; Pople, J. A. Chem. Phys. Lett. 1997, 270, 419. https://doi.org/10.1016/S0009-2614(97)00399-0
  11. Johnson, B. G.; Gill, P. M. W.; Pople, J. A. J. Chem. Phys. 1993, 98, 5612. https://doi.org/10.1063/1.464906
  12. Kappe, A. K.; Casewit, C. J. In Molecular Mechanics Across Chemistry; University Science Books California, 1997.
  13. Sadlej, J. In Semi-Empirical Methods of Quantum Chemistry; Ellis Horwood, Ltd., Chichester, 1985.
  14. Gaussian 03, Revision B.05, Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Montgomery, J. A., Jr.; Vreven, T.; Kudin, K. N.; Burant, J. C.; Millam, J. M.; Iyengar, S. S.; Tomasi, J.; Barone, V.; Mennucci, B.; Cossi, M.; Scalmani, G.; Rega, N.; Petersson, G. A.; Nakatsuji, H.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Klene, M.; Li, X.; Knox, J. E.; Hratchian, H. P.; Cross, J. B.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.; Pomelli, C.; Ochterski, J. W.; Ayala, P. Y.; Morokuma, K.; Voth, G. A.; Salvador, P.; Dannenberg, J. J.; Zakrzewski, V. G.; Dapprich, S.; Daniels, A. D.; Strain, M. C.; Farkas, O.; Malick, D. K.; Rabuck, A. D.; Raghavachari, K.; Foresman, J. B.; Ortiz, J. V.; Cui, Q.; Baboul, A. G.; Clifford, S.; Cioslowski, J.; Stefanov, B. B.; Liu, G.; Liashenko, A.; Piskorz, P.; Komaromi, I.; Martin, R. L.; Fox, D. J.; Keith, T.; Al-Laham, M. A.; Peng, C. Y.; Nanayakkara, A.; Challacombe, M.; Gill, P. M. W.; Johnson, B.; Chen, W.; Wong, M. W.; Gonzalez, C.; Pople, J. A. Gaussian, Inc.: Wallingford, CT, 2003.
  15. Frish, A., Nielsen, A. B., Holder, A. J. GaussView 3.0, Gaussian, Inc.: Pittsburgh, PA, 2003.
  16. Politzer, P.; Murray, J. S.; Grice, M. E.; Desalvo, M.; Miller, E. Mol. Phys. 1997, 91, 923. https://doi.org/10.1080/002689797171030
  17. Murray, J. S.; Brinck, T.; Politzer, P. Chem. Phys. 1996, 204, 289. https://doi.org/10.1016/0301-0104(95)00297-9

Cited by

  1. Comparative Study on the Gas Phase Heats of Formation vol.36, pp.5, 2015, https://doi.org/10.1002/bkcs.10276
  2. A quantitative structure–property relationship (QSPR) study of singlet oxygen generation by pteridines vol.15, pp.6, 2016, https://doi.org/10.1039/C6PP00084C
  3. Prediction of Crystal Density and Explosive Performance of High-Energy-Density Molecules Using the Modified MSEP Scheme vol.37, pp.10, 2016, https://doi.org/10.1002/bkcs.10928
  4. Assessment of density prediction methods based on molecular surface electrostatic potential vol.24, pp.7, 2018, https://doi.org/10.1007/s00894-018-3702-z
  5. Predicting collision‐induced dissociation spectra: Semi‐empirical calculations as a rapid and effective tool in software‐aided mass spectral interpretation vol.28, pp.10, 2013, https://doi.org/10.1002/rcm.6870
  6. Assessment of Recent Researches for Reliable Prediction of Density of Organic Compounds as well as Ionic Liquids and Salts Containing Energetic Groups at Room Temperature vol.45, pp.11, 2020, https://doi.org/10.1002/prep.202000076