Skip to main content
  • Poster presentation
  • Open access
  • Published:

QSPR designer – a program to design and evaluate QSPR models. Case study on pKaprediction

Nowadays, a large amount of experimental and predicted data about the 3D structure of organic molecules and biomolecules is available. Advanced computational methods and high performance computers allow us to obtain large sets of descriptors that can be used to estimate physicochemical properties. It is often of interest to study the correlations between descriptors and properties using multilinear regression and to design, parameterize, and test different QSPR (Quantitative Structure Property Relationship) models.

We developed a modular and easily extensible program, called QSPR Designer, which can read or calculate structural properties of atoms and bonds, employ them as QSPR descriptors, and evaluate correlations between the descriptors and the examined physicochemical property of a molecule. Furthermore, the software allows us to effectively design and parameterize QSPR models, calculate physicochemical properties via the models, test the quality of the models, and provide graphs and tables summarizing the results.

The performance of the software is demonstrated by a case study on the prediction of pKa. The pKa is of fundamental relevance for chemical, biological and pharmaceutical research, because many important physicochemical properties are pKa dependent. Unfortunately, pKa is also one of the most challenging properties to calculate [1]. Atomic charges have proven very successful descriptors for the prediction of pKa[2]. Charges can be calculated using a variety of methods (HF, MP2, functionals, etc.), population analyses (Mulliken, ESP, NPA, etc.) and basis sets. Consequently, the procedure of charge calculation strongly influences their correlation with pKa [3]. Using the QSPR Designer, we have successfully designed, evaluated, and compared 75 different QSPR models for the prediction of pKa from charges. Our best model predicted the pKa for 143 phenols with a correlation coefficient 0.969, RMSE (root mean square error) 0.416 and the average pKa error 0.329.


  1. Lee AC, Crippen GM: Predicting pKa. J Chem Inf Model. 2009, 49: 2013-2033. 10.1021/ci900209w.

    Article  CAS  Google Scholar 

  2. Dixon SL, Jurs PC: Estimation of pKa for organic oxyacids using calculated atomic charges. J Comput Chem. 1993, 12: 1460-1467. 10.1002/jcc.540141208.

    Article  Google Scholar 

  3. Gross KC, Seybold PG, Hadad CM: Comparison of different atomic charge schemes for predicting pKa variations in substituted anilines and phenols. Int J Quantum Chem. 2001, 85: 569-579. 10.1002/qua.1525.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to RS Vařeková.

Rights and permissions

Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License ( ), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Reprints and permissions

About this article

Cite this article

Skřehota, O., Vařeková, R., Geidl, S. et al. QSPR designer – a program to design and evaluate QSPR models. Case study on pKaprediction. J Cheminform 3 (Suppl 1), P16 (2011).

Download citation

  • Published:

  • DOI: