Performance comparison prediction of energy states of atoms in doxorubin and docetaxel using various computational simulations for cancer treatment

  • Authors

    • Bipin Nair B.J
    • Vishnu K.V
    https://doi.org/10.14419/ijet.v7i1.9.9754

    Received date: February 26, 2018

    Accepted date: February 26, 2018

    Published date: March 1, 2018

  • Molecular Energy, Free Energy, Virtual Screening
  • Abstract

    Every atoms or compounds must have ground state as well as excited state when they are participating in chemical reactions. Drug is also a chemical compound which also has the two states. Computing the physical properties of a drug compound and calculate its energy states and Comparing the ground state and excited state performance of aromatic cyclic cancer drugs like doxorubicin and docetaxel using various types of cheminformatics methods.in our work we are using so many physical properties like surface area of an atom etc. to check the efficiency of drug.

  • References

    1. Houk & paul ha-yeon cheong, (2009),” computational prediction of small-molecule catalysts” google scholar.
    2. Jerry w. cubbage and william s. jenks (2001) “computational studies of the ground and excited state potentials of DMSO and H2SO: rele-vance to photostereomutation”.
    3. Eugene j. lynch, amy l. speelman, Bryce a. curry, Charles s. murillo, and jason g. Gillmor, (2012),” expanding and testing a computational method for predicting the ground state reduction potentials of organic molecules on the basis of empirical correlation to experiment”.
    4. Schoendorff, G., Morris, A. R., Hu, E. D., & Wilson, A. K. (2015). A computational study on the ground and excited states of nickel sili-cide. The Journal of Physical Chemistry A, 119(37), 9630-9635. https://doi.org/10.1021/acs.jpca.5b05661.
    5. Vanommeslaeghe, K., Hatcher, E., Acharya, C., Kundu, S., Zhong, S., Shim, J., & Mackerell, A. D. (2010). CHARMM general force field: A force field for drug‐like molecules compatible with the CHARMM all‐atom additive biological force fields. Journal of com-putational chemistry, 31(4), 671-690,
    6. Vergara-Jaque, A., Comer, J., Monsalve, L., González-Nilo, F. D., & Sandoval, C. (2013). Computationally efficient methodology for atomic-level characterization of dendrimer–drug complexes: a com-parison of amine-and acetyl-terminated PAMAM. The Journal of Physical Chemistry B, 117(22), 6801-6813. https://doi.org/10.1021/jp4000363.
    7. Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., & Kanehisa, M. (2008). Prediction of drug–target interaction networks from the integration of chemical and genomic spaces. Bioinformatics, 24(13), i232-i240. https://doi.org/10.1093/bioinformatics/btn162.
    8. Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., & Kanehisa, M. (2008). Prediction of drug–target interaction networks from the integration of chemical and genomic spaces. Bioinformatics, 24(13), i232-i240. https://doi.org/10.1093/bioinformatics/btn162.
    9. Blancafort, L., Cohen, B., Hare, P. M., Kohler, B., & Robb, M. A. (2005). Singlet excited-state dynamics of 5-fluorocytosine and cyto-sine: An experimental and computational study. The Journal of Phys-ical Chemistry A, 109(20), 4431-4436. https://doi.org/10.1021/jp045614v.
    10. Ertl, P., Rohde, B., & Selzer, P. (2000). Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. Journal of medicinal chemistry, 43(20), 3714-3717. https://doi.org/10.1021/jm000942e.
    11. Stsiapura, V. I., Maskevich, A. A., Kuzmitsky, V. A., Turoverov, K. K., & Kuznetsova, I. M. (2007). Computational study of thioflavin T torsional relaxation in the excited state. The Journal of Physical Chemistry A, 111(22), 4829-4835. https://doi.org/10.1021/jp070590o.
    12. Improta, R., Barone, V., Scalmani, G., & Frisch, M. J. (2006). A state-specific polarizable continuum model time dependent density functional theory method for excited state calculations in solu-tion. The Journal of chemical physics, 125(5), 054103. https://doi.org/10.1063/1.2222364.
    13. Cramer, C. J., & Falvey, D. E. (1997). Computational prediction of a ground-state triplet arylnitrenium ion and a possible ground-state tri-plet silylene. Tetrahedron letters, 38(9), 1515-1518. https://doi.org/10.1016/S0040-4039(97)00126-3.
    14. prediction of drug response in breast cancer using integrative experi-mental/computational modeling(2009)
    15. Doemer, M., Guglielmi, M., Athri, P., Nagornova, N. S., Rizzo, T. R., Boyarkin, O. V., & Rothlisberger, U. (2013). Assessing the per-formance of computational methods for the prediction of the ground state structure of a cyclic decapeptide. International Journal of Quantum Chemistry, 113(6), 808-814. https://doi.org/10.1002/qua.24085.
    16. Gopal, K. V., NAMBOORI, P. K., Premkumar, P., Gopakumar, D., & Narayanan, B. S. (2011). Insilico Modeling and Simulation of Magnetic Nanoparticles for the Biological Cell Isolation Tech-nique. International Journal of Nanoscience, 10(01n02), 323-327. https://doi.org/10.1142/S0219581X11008022
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  • How to Cite

    Nair B.J, B., & K.V, V. (2018). Performance comparison prediction of energy states of atoms in doxorubin and docetaxel using various computational simulations for cancer treatment. International Journal of Engineering and Technology, 7(1.9), 157-161. https://doi.org/10.14419/ijet.v7i1.9.9754