Effect of Different Factors on Predicting Constants of Acidity of Low-Molecular Organic Compounds by Means of Machine Learning

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Abstract

A study is performed of the effect the way of standardizing the molecular structure and parameters of calculating molecular fingerprints has on the accuracy of predicting constants of acidity. It is shown that standardization (i.e., the choice of the tautomeric form and the way of writing the structure of the molecule) using OpenEye QuacPac gives the best results, but the RDKit library allows comparable accuracy to be achieved. It is established that how the charge state is chosen has a great effect on the accuracy of predictions. The accuracy of predictions depending on the radius (size of substructures) of circular molecular fingerprints is studied, and the best results are achieved using radius r = 2. A random forest, a machine learning algorithm, is used. It is also shown that the use of support vectors ensures fairly high accuracy when optimizing hyperparameters.

About the authors

D. D. Matyushin

Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences

Email: shonastya@yandex.ru
119071, Moscow, Russia

A. Yu. Sholokhova

Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences

Email: shonastya@yandex.ru
119071, Moscow, Russia

A. K. Buryak

Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences

Author for correspondence.
Email: shonastya@yandex.ru
119071, Moscow, Russia

References

  1. Baltruschat M., Czodrowski P. // F1000Res. 2020. V. 9. P. 113. https://doi.org/10.12688/f1000research.22090.2
  2. Mansouri K., Cariello N.F., Korotcov A. et al. // J. Cheminform. 2019. V. 11. № 1. P. 60. https://doi.org/10.1186/s13321-019-0384-1
  3. Mayr F., Wieder M., Wieder O. et al. // Front. Chem. 2022. V. 10. P. 866585. https://doi.org/10.3389/fchem.2022.866585
  4. Lu Y., Anand S., Shirley W. et al. // J. Chem. Inf. Model. 2019. V. 59. № 11. P. 4706. https://doi.org/10.1021/acs.jcim.9b00498
  5. Rupp M., Korner R., Tetko I. // CCHTS. 2011. V. 14. № 5. P. 307. https://doi.org/10.2174/138620711795508403
  6. Lionta E., Spyrou G., Vassilatis D. et al. // CTMC. 2014. V. 14. № 16. P. 1923. https://doi.org/10.2174/1568026614666140929124445
  7. Bahi M., Batouche M. // 2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS). Tebessa: IEEE, 2018. P. 1–5. https://doi.org/10.1109/PAIS.2018.8598488
  8. Yang Q., Ji H., Fan X. et al. // J. Chromatogr. A. 2021. V. 1656. P. 462536. https://doi.org/10.1016/j.chroma.2021.462536
  9. Fedorova E.S., Matyushin D.D., Plyushchenko I.V. et al. // J. Chromatogr. A. 2022. V. 1664. P. 462792. https://doi.org/10.1016/j.chroma.2021.462792
  10. Milyushkin A.L., Matyushin D.D., Buryak A.K. // J. Chromatogr. A. 2020. V. 1613. P. 460724. https://doi.org/10.1016/j.chroma.2019.460724
  11. Zenkevich I.G., Nikitina D.A. // Russ. J. Phys. Chem. A. 2021. V. 95. № 2. P. 395. https://doi.org/ Зенкевич И.Г., Никитина Д.А. // Журн. физ. химии. 2021. Т. 95. № 2. С. 285.https://doi.org/10.1134/S003602442102028X
  12. Angra S., Ahuja S. // 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC). Chirala, Andhra Pradesh, India: IEEE, 2017. P. 57. https://doi.org/10.1109/ICBDACI.2017.8070809
  13. Mansouri K., Grulke C.M., Judson R.S. et al. // J. Cheminform. 2018. V. 10. № 1. P. 10. https://doi.org/10.1186/s13321-018-0263-1
  14. Parmar A., Katariya R., Patel V. // International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018 / Ed. Hemanth J., Fernando X., Lafata P. et al. Cham: Springer International Publishing, 2019. V. 26. P. 758. https://doi.org/10.1007/978-3-030-03146-6_86
  15. Cereto-Massagué A., Ojeda M.J., Valls C. et al. // Methods. 2015. V. 71. P. 58. https://doi.org/10.1016/j.ymeth.2014.08.005
  16. Rogers D., Hahn M. // J. Chem. Inf. Model. 2010. V. 50. № 5. P. 742. https://doi.org/10.1021/ci100050t
  17. Xiong J., Li Z., Wang G. et al. // Bioinformatics / Ed. by Z. Lu. 2022. V. 38. № 3. P. 792. https://doi.org/10.1093/bioinformatics/btab714
  18. Pan X., Wang H., Li C. et al. // J. Chem. Inf. Model. 2021. V. 61. № 7. P. 3159. https://doi.org/10.1021/acs.jcim.1c00075
  19. Reza Ghiasi, Zamani A., Shamami M.K. // Russ. J. Phys. Chem. A. 2019. V. 93. № 8. P. 1537. https://doi.org/10.1134/S0036024419080247
  20. Prasad S., Huang J., Zeng Q. et al. // J. Comput. Aided Mol. Des. 2018. V. 32. № 10. P. 1191. https://doi.org/10.1007/s10822-018-0167-1
  21. Pracht P., Wilcken R., Udvarhelyi A. et al. // J. Comput. Aided Mol. Des. 2018. V. 32. № 10. P. 1139. https://doi.org/10.1007/s10822-018-0145-7
  22. Pedregosa F., Varoquaux G., Gramfort A. et al. Scikit-learn: Machine Learning in Python: arXiv:1201.0490. arXiv, 2018. https://arxiv.org/abs/1201.0490
  23. Bento A.P., Hersey A., Félix E. et al. // J. Cheminform. 2020. V. 12. № 1. P. 51. https://doi.org/10.1186/s13321-020-00456-1
  24. Chang C.-C., Lin C.-J. // ACM Trans. Intell. Syst. Technol. 2011. V. 2. № 3. P. 1. https://doi.org/10.1145/1961189.1961199
  25. Willighagen E.L., Mayfield J.W., Alvarsson J. et al. // J. Cheminform. 2017. V. 9. № 1. P. 33. https://doi.org/10.1186/s13321-017-0220-4
  26. https://github.com/czodrowskilab/Machine-learning-meets-pKa
  27. Heller S., McNaught A., Stein S. et al. // J. Cheminform. 2013. V. 5. № 1. P. 7. https://doi.org/10.1186/1758-2946-5-7
  28. Matyushin D.D., Buryak A.K. // IEEE Access. 2020. V. 8. P. 223140. https://doi.org/10.1109/ACCESS.2020.3045047

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Copyright (c) 2023 Д.Д. Матюшин, А.Ю. Шолохова, А.К. Буряк