Investigation of strategies for the interclass prediction of the activity of bipharmacophore butyrylcholinesterase inhibitors based on QSAR modeling

Capa

Citar

Texto integral

Acesso aberto Acesso aberto
Acesso é fechado Acesso está concedido
Acesso é fechado Acesso é pago ou somente para assinantes

Resumo

Three schemes of interclass prediction of the activity of a number of bipharmacophoric butyrylcholinesterase inhibitors were studied using QSAR modeling. Using machine learning methods (multiple linear regression, random forest, support vector machine and Gaussian process), QSAR models with satisfactory statistical characteristics were constructed. Based on them, rational and random interclass prediction schemes were studied. It was found that these schemes complement each other and their relative efficiency was assessed.

Texto integral

Acesso é fechado

Sobre autores

V. Grigorev

Institute of Physiologically Active Compounds, Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry of the Russian Academy of Sciences

Autor responsável pela correspondência
Email: beng@ipac.ac.ru
ORCID ID: 0000-0002-5288-3242
Rússia, 142432, Chernogolovka

A. Razdolsky

Institute of Physiologically Active Compounds, Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry of the Russian Academy of Sciences

Email: beng@ipac.ac.ru
ORCID ID: 0000-0002-3389-4659
Rússia, 142432, Chernogolovka

V. Kazachenko

Institute of Physiologically Active Compounds, Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry of the Russian Academy of Sciences

Email: beng@ipac.ac.ru
ORCID ID: 0000-0003-1424-1895
Rússia, 142432, Chernogolovka

Bibliografia

  1. Schlander M., Hernandez-Villafuerte K., Cheng C.Y., Mestre-Ferrandiz J., Baumann M. // Pharmacoeconomics. 2021. Vol. 39. P. 1243. doi: 10.1007/s40273-021-01065-y
  2. Sadybekov A.V., Katritch V. // Nature. 2023. Vol. 616. P. 673. doi: 10.1038/s41586-023-05905-z
  3. Doytchinova I. // Molecules. 2022. Vol. 27. P. 1496. doi: 10.3390/molecules27051496
  4. Niazi S.K., Mariam Z. // Pharmaceuticals. 2024. Vol. 17. P. 22. doi: 10.3390/ph17010022
  5. Baig M.H., Ahmad K., Roy S., Ashraf J.M., Adil M., Siddiqui M.H., Khan S., Kamal M.A., Provazník I., Choi I. // Curr. Pharm. Des. 2016. Vol. 22. P. 572. doi 10.2174/ 1381612822666151125000550
  6. Зефирова О.Н., Зефиров Н.С. // Вестн. Московск. унив. Сер. 2. Химия. 2000. Т. 41. С. 103.
  7. Hu Y., Stumpfe D., Bajorath J. // J. Med. Chem. 2017. Vol. 60. P. 1238. doi: 10.1021/acs.jmedchem.6b01437
  8. Stojanović L., Popović M., Tijanić N., Rakočević G., Kalinić M. // J. Chem. Inf. Model. 2020. Vol. 60. P. 4629. doi: 10.1021/acs.jcim.0c00622
  9. Acharya A., Yadav M., Nagpure M., Kumaresan S., Guchhait S.K. // Drug Discov. Today. 2024. Vol. 29. Article no. 103845. doi: 10.1016/j.drudis.2023.103845
  10. Wang Y., Jia S., Wang F., Jiang R., Yin X., Wang S., Jin R., Guo H., Tang Y., Wang Y. // Int. J. Mol. Sci. 2024. Vol. 25. Article no. 7434. doi: 10.3390/ijms25137434
  11. Floresta G., Rescifina A., Marrazzo A., Dichiara M., Pistarà V., Pittalà V., Prezzavento O., Amata E. // Eur. J. Med. Chem. 2017. Vol. 139. P. 884. doi 10.1016/ j.ejmech.2017.08.053
  12. Škuta C., Cortés-Ciriano I., Dehaen W., Kříž P., van Westen G.J.P., Tetko I.V., Bender A., Svozil D. // J. Cheminform. 2020. Vol. 12. P. 39. doi: 10.1186/s13321-020-00443-6
  13. Zheng S., Lei Z., Ai H., Chen H., Deng D., Yang Y. // J. Cheminform. 2021. Vol. 13. P. 87. doi: 10.1186/s13321-021-00565-5
  14. Ryszkiewicz P., Malinowska B., Schlicker E. // Pharmacol. Rep. 2023. Vol. 75. P. 755. doi: 10.1007/s43440-023-00501-4
  15. Sánchez-Tejeda J.F., Sánchez-Ruiz J.F., Salazar J.R., Loza-Mejía M.A. // Front. Chem. 2020. Vol. 8. P. 176. doi: 10.3389/fchem.2020.00176
  16. Albertini C., Salerno A., de Sena Murteira Pinheiro P., Bolognesi M.L. // Med. Res. Rev. 2021. Vol. 41. P. 2606. doi: 10.1002/med.21699
  17. Zhou S., Huang G. // Biomed. Pharmacother. 2022. Vol. 146. Article no. 112556. doi: 10.1016/j.biopha. 2021.112556
  18. Greig N.H., Lahiri D.K., Sambamurti K. // Int. Psychogeriatr. 2002. Vol. 14. P. 77. doi: 10.1017/s1041610203008676
  19. Makhaeva G.F., Shevtsova E.F., Boltneva N.P., Lushchekina S.V., Kovaleva N.V., Rudakova E.V., Bachurin S.O., Rudy J. Richardson R.J. // Chem. Biol. Interact. 2019. Vol. 308. P. 224. doi: 10.1016/j.cbi.2019.05.020
  20. Bachurin S.O., Makhaeva G.F., Shevtsova E.F., Aksinenko A.Y., Grigoriev V.V., Shevtsov P.N., Goreva T.V., Epishina T.A., Kovaleva N.V., Pushkareva E.A., Boltneva N.P., Lushchekina S.V., Gabrelyan A.V., Zamoyski V.L., Dubova L.G., Rudakova E.V., Fisenko V.P., Bovina E.V., Richardson R.J. // Molecules. 2021. Vol. 26. P. 5527. doi: 10.3390/molecules26185527
  21. Kiralj R., Ferreira M.M.C. // J. Braz. Chem. Soc. 2009. Vol. 20. P. 770. doi: 10.1590/S0103-50532009000400021
  22. Tropsha A., Gramatica P., Gombar V.K. // QSAR Comb. Sci. 2003. Vol. 22. P. 69. doi: 10.1002/qsar.200390007
  23. Kumar S., Manoharan A., Jayalakshmi J., Abdelgawad M.A., Mahdi W.A., Alshehri S., Ghoneim M.M., Pappachen L.K., Zachariah S.M., Aneesh T.P., Mathew B. // RSC Adv. 2023.Vol. 13. P. 9513. doi: 10.1039/d3ra00526g
  24. Pang X., Fu H., Yang S., Wang L., Liu A.-L., Wu S., Du G.-H. // Molecules. 2017. Vol. 22. P. 1254. doi: 10.3390/molecules22081254
  25. Fortran Numerical Library. https://developer.nvidia.com/imsl-fortran-numerical-library?display=default
  26. Random Forest. http://www.stat.berkeley.edu/~breiman/RandomForests/cc_examples/prog.f
  27. Suykens J.A.K., Vandewalle J. // Neural Process. Lett. 1999. Vol. 9. P. 293. doi: 10.1023/A:1018628609742
  28. Gaussian Processes for Machine Learning. http://gaussianprocess.org/gpml/
  29. Landrum G.A., Riniker S. // J. Chem. Inf. Model. 2024. Vol. 64. P. 1560. doi: 10.1021/acs.jcim.4c00049
  30. Mitra I., Saha A., Roy K. // Mol. Simul. 2010. Vol. 36. P. 1067. doi: 10.1080/08927022.2010.503326
  31. Kubinyi H. // Quant. Struct. Act. Relat. 1994. Vol. 13. P. 285. doi: 10.1002/qsar.19940130306
  32. Willett P., Barnard J.M., Downs G.M. // J. Chem. Inf. Comput. Sci. 1998. Vol. 38. P. 983. doi: 10.1021/ci9800211
  33. Раздольский А.Н., Казаченко В.П., Страхова Н.Н., Григорьев В.Ю. // Современные наукоемкие технологии. 2023. Вып. 10. С. 63. doi: 10.17513/snt.39792
  34. Trepalin S.V., Razdolskii A.N., Raevskii O.A. // Pharm. Chem. J. 2000. Vol. 34. P. 650. doi 10.1023/ A:1010499601434

Arquivos suplementares

Arquivos suplementares
Ação
1. JATS XML
Baixar
2. Scheme 1.

Baixar (196KB)
Metadados
3. Fig. 1. The first strategy of interclass forecasting (ICP-1).

Baixar (9KB)
Metadados
4. Fig. 2. The second strategy of interclass forecasting (ICP-2).

Baixar (7KB)
Metadados
5. Fig. 3. Frequency of occurrence of group descriptors in QSAR models (MPA-1).

Baixar (1KB)
Metadados
6. Fig. 4. Frequency of occurrence of group descriptors in QSAR models (MPA-2).

Baixar (1KB)
Metadados
7. Fig. 5. Relationship between experimental and predicted values ​​of compound activity.

Baixar (3KB)
Metadados
8. Fig. 6. Frequency of occurrence of group descriptors in QSAR models (MPA-3).

Baixar (1KB)
Metadados

Declaração de direitos autorais © Russian Academy of Sciences, 2024