Autors: Sapundzhi F., Georgiev S., Lazarova, M. D., Altaparmakov, I. L., Todorov V., Ivanova A.
Title: Intelligent Analysis of Mechanistic Models and Docking in Biomolecule Research
Keywords: Biological compounds, Growth and Decay, Mechanistic models, Molecule docking, Pharmacokinetics

Abstract: Mechanistic models in biology offer a quantitative framework for describing and analyzing biological processes. These models enable the integration of accumulated knowledge and support the accurate prediction and optimization of biologically active compounds. In this study, we provide an overview of several widely used mechanistic models related to growth and decay, highlighting their properties that can be effectively applied in molecular docking. We propose a novel approach that integrates mechanistic modeling with docking analysis, offering a statistically robust method for optimizing bioactive analogs. This approach has the potential to facilitate the design of new compounds with improved pharmacokinetic profiles.

References

  1. Anguelov, R., Borisov, M., Iliev, A., Kyurkchiev, N., Markov, S.: On the chemical meaning of some growth models possessing Gompertzian-type property. Math Meth Appl Sci. (2017). https://doi.org/10.1002/mma.4539
  2. Chellaboina, V., Bhat, S.P., Haddat, W.M., Bernstein, D.S.: Modeling and analysis of massaction kinetics. IEEE Contr. Syst. Mag. 29, 60–78 (2009)
  3. Gompertz, B.: On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. Phil. Trans. R. Soc. Lond. 115, 513–585 (1825)
  4. Garfinkel, A., Shevtsov, J., Guo, Y.: Modeling Life. The Mathematics of Biological Systems. Springer, Heidelberg (2018)
  5. Goriely, A.: The Mathematics and Mechanics of Biological Growth. Springer, Heidelberg (2017)
  6. Lente, G.: Deterministic kinetics in chemistry and systems biology. In: Briefs in Molecular Science. Springer, Cham (2015)
  7. Markov, S., Iliev, A., Rahnev, A., Kyurkchiev, N.: On the exponential–generalized extended Compertz cumulative sigmoid. Int. J. Pure Appl. Math. 120(4), 555–562 (2018)
  8. Markov, S.: Reaction networks reveal new links between Gompertz and Verhulst growth functions. Biomath 8, 1904167 (2019)
  9. Markov, S.: On a class of generalized Gompertz-Bateman growth–decay models. Biomath. Commun. 6(1), 51–64 (2019)
  10. Murray, J.D.: Mathematical Biology: I. An Introduction, 3rd edn. Springer-Verlag, New York (2002)
  11. Verhulst, P.F.: Notice sur la loi que la population poursuit dans son accroissement. Correspondance mathmatique et physique 10, 113–121 (1838)
  12. Winsor, C.: Gompertz curve as a growth equation. Proc. Nat. Acad. Sciences 18, 1–8 (1932)
  13. Anguelov R., Borisov M. On the chemical meaning of some growth models possessing Gompertzian-type property. Math Meth Appl Sci., 1–12 (2017)
  14. Banks, H.T.: Mechanistic models of Growth and Decay, Center for research in Scientific Computation. Center for Quantative Sciences in Biomedicine North Carolina State University, Raleigh, NC (2017)
  15. Lazarova, M., Markov, S., Vassilev, A.: On some classes of growth functions and their links toreaction network theory. AIP Conf. Proc. 2302, 080004 (2020)
  16. Focke, W.W., van der Westhuizen, I., Musee, N., et al.: Kinetic interpretation of log-logistic dose-time response curves. Sci. Rep. 7, 2234 (2017)
  17. Asitok, A., et al.: Overproduction of a thermo-stable halo-alkaline protease on agrowaste-based optimized medium through alternate combinatorial random mutagenesis of Stenotrophomonas acidaminiphila. Biotechnol. Rep. 35, e00746 (2022)
  18. Rochayani, M.Y., Menufandu, D.G.R., Dapa, R.: Investigating the growth of bacteria using double sigmoid model with reparameterization. Int. J. Glob. Optim. Appl. 2(4), 200–208 (2023)
  19. Tjørve, K.M.C., Tjørve, E.: The use of Gompertz models in growth analyses, and new Gompertz-model approach: an addition to the unified-Richards family. PLoS ONE 12(6), e0178691 (2017)
  20. Vaghi, C., et al.: Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors. PLoS Comput. Biol. 16(2), e1007178 (2020)
  21. Martin, L.J.: State of the art iterative docking with logistic regression and Morgan fingerprints. ChemRxiv (2021). https://doi.org/10.26434/chemrxiv.14348117.v1
  22. Antontsev, V., Jagarapu, A., Bundey, Y., et al.: A hybrid modeling approach for assessing mechanistic models of small molecule partitioning in vivo using a machine learning-integrated modeling platform. Sci. Rep. 11, 11143 (2021)

Issue

Lecture Notes in Networks and Systems, vol. 1530 LNNS, pp. 407-414, 2025, Switzerland, https://doi.org/10.1007/978-3-031-98565-2_45

Вид: книга/глава(и) от книга, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science