Neutral hydrolysis rate constant




The Neutral hydrolysis rate constant modelpredicts hydrolysis products of discrete organic chemicals under the following experimental conditions: neutral pH (6.5-7.4), temperature 20-35°C and atmospheric pressure. The modeled endpoint is the quantities of the parent chemical and its transformation products (mol/mol parent) at the 28th day as a result of chemical reaction with water.




The training set of the model consists of 341 chemicals, their experimental hydrolysis rate constants and their transformation products. The following classes of chemicals are included in the model: epoxides, aziridines, esters, carbamates, halomethanes, selected alkyl halides, anhydrides, dithiocarbamates, isocyanates, isothiocyanates, sulfonyl chloride, lactones, nitriles, amides, N-halamines, carbamates, organic peroxides, etc. The documented pathways of hydrolysis were collected from various scientific sources, including articles and public web sites. The model simulates hydrolysis under test conditions: nearly neutral pH, 20-35°C and atmospheric pressure [1-8].




A set of 183 hydrolysis transformations was extracted from the experimental data. The probabilities of the transformations were estimated by non-linear least square method:


 Equ _ni1


where QObs and QCalc are observed and predicted quantities, respectively, and P is a vector of estimated probabilities of transformations. Further details on the mathematical formalism of the model can be reviewed in [9, 10].




A stepwise approach [11] was used to define the applicability domain of the model. It consists of the following sub-domain levels:


  • General parametric requirements - includes ranges of variation log KOW,  WS and MW,
  • Structural domain - based on atom-centered fragments (ACFs).


A chemical is considered In Domain if its log KOW, WS and MW are within the specified ranges and if its ACFs are presented in the training chemicals. The information implemented in the applicability domain is extracted from the correctly predicted training chemicals used to build the model and in this respect, the applicability domain determines practically the interpolation space of the model.




The goodness of fit evaluated by the squared coefficient of correlation is R2 = 0.80




The model provides results for:


  • Quantities of parent and products as a result of hydrolysis, mol/mol parent,
  • Hydrolysis rate constant, d-1,
  • Applicability domain details.




  1. B. Chen, Environ. Eng. Sci., 28(6), 2011, pp.385-394.
  2. W. Mabey, T. Mill, J. Phys. Chem. Ref. Data, 1978, 7(2), pp.383-415.
  3. N.Jain, S.Machata, E. Tabibi, S. Yalkowsky, AAPS Pharm. Sci. Tech., 2007, 8(1), pp.E1-E5.
  4. Metabolic Pathways of AgroChemicals: Part 2:Insecticides and Fungicides, T.R. Roberts, D.H. Hutson, (Eds.), Royal Society of Chemistry, Cambridge, 1999.
  5. J. Snyder, L. Stock, J. Org. Chem., 1980, 45(10), pp.1990-1999.
  6. P.M. Jeffers, L.M. Ward, L.M. Woytowitch, N.L. Wolfe, Environ. Sci. Technol., 1989, 23(8), pp.965-970.
  7. K.J. McNeil, J.A. DiCaprio, D.A. Walsh, R.F. Pratt, J. Am. Chem. Soc., 1980, 102(6), pp.1859-1865.
  8.    A. Queen,Can. J. Chem., 1967, 45(14), pp.1619-1629.
  9. S. Dimitrov, T. Pavlov, G. Veith, O. Mekenyan, SAR and QSAR in Environ Res, 2011, 22, pp.699-718.
  10. S. Dimitrov, T. Pavlov, N. Dimitrova, D. Georgieva, D. Nedelcheva, A. Kesova, R. Vasilev, O. Mekenyan, SAR and QSAR in Environ Res, 2011, 22, pp.719-755.
  11. S. Dimitrov, G. Dimitrova, T. Pavlov, N. Dimitrova, G. Patlevisz, J. Niemela and O. Mekenyan, J. Chem. Inf. Model., 2005, 45, pp.839-849.



Model features


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