Journal of Computer-Aided Molecular Design

, Volume 24, Issue 1, pp 17–22 | Cite as

How the energy evaluation method used in the geometry optimization step affect the quality of the subsequent QSAR/QSPR models

  • Åsmund Rinnan
  • Niels Johan Christensen
  • Søren Balling Engelsen


The quantitative influence of the choice of energy evaluation method used in the geometry optimization step prior to the calculation of molecular descriptors in QSAR and QSPR models was investigated. A total of 11 energy evaluation methods on three molecular datasets (toxicological compounds, aromatic compounds and PPARγ agonists) were studied. The methods employed were: MMFF94 s, MM3* with ε r (relative dielectric constant) = 1, MM3* with ε r  = 80, AM1, PM3, HF/STO-3G, HF/6-31G, HF/6-31G(d,p), B3LYP/STO-3G, B3LYP/6-31G, and B3LYP/6-31G(d,p). The 3D-descriptors used in the QSAR/QSPR models were calculated with commercially available molecular descriptor programs primarily directed toward pharmaceutical research. In order to evaluate the uncertainties involved in the QSAR/QSPR predictions bootstrapping was used to validate all models using 1,000 drawings for each data set. The scale free error-term, q 2 , was used to compare the relative quality of the models resulting from different optimization methods on the same set of molecules. Depending on the dataset, the average 0.632 bootstrap estimated q 2 varies from 0.55 to 0.57 for the toxicological compounds, from 0.58 to 0.62 for the aromatic compounds, and from 0.69 to 0.75 for the PPARγ agonists. The B3LYP/6-31G(d,p) provided the best overall results, albeit the increase in q 2 was small in all cases. The results clearly indicate that the choice of the energy evaluation method has very limited impact. This study suggests that QSAR or QSPR studies might benefit from the choice of a rapid optimization method with little or no loss in model accuracy.


QSAR QSPR Energy evaluation PLS regression Quantum mechanics Semi-empirical Molecular mechanics 



Quantitative structure property relationship


Quantitative structure activity relationship


Allinger’s molecular mechanics


Austin model 1


Parameterized model 3, HF, Hartree–Fock


The hybrid exchange–correlation functional based on work from Becke, Lee, Yang and Par


Partial least squares


Root mean square distance


Monte Carlo multiple minimum



The authors would like to thank the KU-LIFE strategic research project BEST for the financial funding of Åsmund Rinnan and the project Build Your Food ((FFS05-9) sponsored by the Ministry of Agriculture and Fisheries for the financial funding for Niels Johan Christensen.

Supplementary material

10822_2009_9308_MOESM1_ESM.docx (96 kb)
Supplementary material 1 (DOCX 96 kb)


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Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Åsmund Rinnan
    • 1
  • Niels Johan Christensen
    • 1
  • Søren Balling Engelsen
    • 1
  1. 1.Quality and Technology, Department of Food Science, Faculty of Life SciencesUniversity of CopenhagenFrederiksberg CDenmark

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