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Design of New Anilines with Predicted Values of Their Octanol/Water Partition Coefficients

  1. 1 Laboratoire de Thermodynamique et Physico-Chimie du Milieu (LTPCM), Unité de Formation et de Recherche des Sciences Fondamentales et Appliquées (UFR-SFA), Université Nangui ABROGOUA, Abidjan, Côte d’Ivoire
  2. 2 Unité de Formation et de Recherche Sciences et Technologies (UFR-ST), Université Alassane OUATTARA, Bouaké, Côte d’Ivoire
Computational Chemistry·Volume 14, Issue 4·Pages 131–147·Published 22 October 2026·DOI10.68624/cc.2026.144008
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Abstract

In our previous work, we established a predictive QSPR (Quantitative Structure-Property Relationship) model for the octanol/water partition coefficient based on ionization energy (IE), electronic energy (ET), and dipole moment (μ). This model has been shown to be suitable for predicting the properties of new anilines falling within its applicability domain, with a 95% confidence level. The present study, which builds upon that earlier work, aims to expand the dataset for this family of compounds by designing new anilines that fit the model’s applicability domain and predicting their octanol/water partition coefficients. In this context, we designed a series of thirty-three (33) new anilines. All these molecules were optimized at the B3LYP/6-311G (d, p) level of theory, and vibrational frequencies were calculated at the same level. The calculated octanol/water partition coefficients for these molecules indicate that they are all lipophilic. The formation reaction for these new compounds proceeds spontaneously, accompanied by heat release and a decrease in disorder.

KeywordsDesignAnilineQSPR ModelOctanol/Water Partition CoefficientStandard Thermodynamic Quantity of Formation
  1. Anjalin, M., Kanagathara, N. and Baby Suganthi, A.R. (2020) A Brief Review on Aniline and Its Derivatives. Materials Today: Proceedings, 33, 4751-4755. https://doi.org/10.1016/j.matpr.2020.08.358
  2. Diarrassouba, F., Bamba, K. and Ziao, N. (2026) Prediction of the Hydrophobic Character of Aromatic Amines by Quantum Chemistry and QSPR Methods: The Case of Aniline and Its Derivatives. Computational Chemistry, 14, 105-130. https://doi.org/10.4236/cc.2026.144007
  3. Dennington, R., Keith, T. and Millam, J. (2009) GaussView Version 5, Semichem Inc., Shawnee Mission.
  4. Frisch, M.J., et al. (2009) Gaussian 09, Revision A.02. Gaussian, Inc.
  5. (2015) ACDLABS 10. Advanced Chemistry Development Inc., Toronto.
  6. Tetko, I.V., Sushko, I., Pandey, A.K., Zhu, H., Tropsha, A., Papa, E., et al. (2008) Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection. Journal of Chemical Information and Modeling, 48, 1733-1746. https://doi.org/10.1021/ci800151m
  7. Jaworska, J., Nikolova-Jeliazkova, N. and Aldenberg, T. (2005) QSAR Applicability Domain Estimation by Projection of the Training Set in Descriptor Space: A Review. Alternatives to Laboratory Animals, 33, 445-459. https://doi.org/10.1177/026119290503300508
  8. Netzeva, T.I., Worth, A.P., Aldenberg, T., Benigni, R., Cronin, M.T.D., Gramatica, P., et al. (2005) Current Status of Methods for Defining the Applicability Domain of (Quantitative) Structure-Activity Relationships. Alternatives to Laboratory Animals, 33, 155-173. https://doi.org/10.1177/026119290503300209
  9. Chase Jr., M.W., Davies, C.A., Downey Jr., J.R., Frurip, D.J., McDonald, R.A. and Syverud, A.N. (1985) JANAF Thermochemical Tables, 3rd Edition. Journal of Physical and Chemical Reference Data, 14.
  10. Ochterski, J.W. (2000) Thermochemistry in Gaussian. Gaussian, Inc.