Molecular modeling in the radiation therapy. The algebraic approach

V.A. Volkov, Yu.H. Tarasich


The rapid development of the chemical industry and science, and new challenges in the healthcare sector, put forward increased demands for the development of the theory of organic and inorganic chemistry, for the search and implementation of new modeling and analysis methods, and for the improvement of technological processes. One of the main challenges at the intersection of chemistry, physics, biol- ogy, medicine, and genetics is the search for new methods and approaches to the diagnosis and treatment of cancer.

A deeper understanding of cancer’s genetics and molecular biology has led to the identification of an increasing number of potential molecular targets that can be used for the discovery and development of anticancer drugs, radiation therapy, etc. One of the main places in this is occupied by molecular modeling. Despite the availability of more and more data on existing proteins and nucleic acids and the availability of modeling methods and tools, the development and use of a wide variety of combined methods and tools for modeling and computing large molecular systems remain an open issue. One of the possible solutions for this problem is the application of the algebraic approach and the corresponding formal methods, which have proven effective in many other fields today.

The main idea of the research is the application of algebraic modeling technology and quantum chemical apparatus for modeling and verification of organic chemistry problems, in particular, modeling and analysis of radiation therapy problems. The paper presents the first steps of the research. The example of the formalization of the synchrotron operation principle and the example of the interaction of protons with substance in the example of the determination/calculation of the physically absorbed dose are given in the paper.

Prombles in programming 2022; 3-4: 231-239


algebraic modeling; proton therapy; molecular modeling; formal methods; insertion modeling

Full Text:


References 2022. The Nobel Prize in Chemistry 2002. [online] Available at: [Accessed 22 June 2022].

Press release: The Nobel Prize in Chemistry 2021. [online] Available at:[Accessed 23 June 2022].

Naqvi, A., Mohammad, T., Hasan, G. and Hassan, M., 2018. Advancements in Docking and Molecular Dynamics Simulations Towards Ligand- receptor Interactions and Structure-function Relationships. Current Topics in Medicinal Chemistry, 18(20), pp.1755-1768. CrossRef

Mathpal, D., Masand, M., Thomas, A., Ahmad, I., Saeed, M., Zaman, G., Kamal, M., Jawaid, T., Sharma, P., Gupta, M., Kumar, S., Sriv- astava, S. and Balaramnavar, V., 2021. Pharmacophore modeling, docking and the integrated use of a ligand- and structure-based virtual screening approach for novel DNA gyrase inhibitors: synthetic and biological evaluation studies. RSC Advances, 11(55), pp.34462- 34478. CrossRef

Torres, P., Sodero, A., Jofily, P. and Silva-Jr, F., 2019. Key Topics in Molecular Docking for Drug Design. International Journal of Molecular Sciences, 20(18), p.4574. CrossRef

Santos, L., Ferreira, R. and Caffarena, E., 2019. Integrating Molecular Docking and Molecular Dynamics Simulations. Methods in Molecular Biology, pp.13-34. CrossRef

Geromichalos GD. Importance of molecular computer modeling in anticancer drug development. J BUON. 2007 Sep;12 Suppl 1:S101-18. PMID: 17935268.

Lin, X., Li, X. and Lin, X., 2020. A Review on Applications of Computational Methods in Drug Screening and Design. Molecules, 25(6), p.1375. CrossRef

Lima, A., Philot, E., Trossini, G., Scott, L., Maltarollo, V. and Honorio, K., 2016. Use of machine learning approaches for novel drug dis- covery. Expert Opinion on Drug Discovery, 11(3), pp.225-239. CrossRef

Joseph-McCarth,y D, Baber, JC, Feyfant, E, Thompson, DC, Humblet ,C. Lead optimization via high-throughput molecular docking. Curr Opin Drug Discov Devel. 2007 May;10(3):264-74

Kaczor, A., Bartuzi, D., Stępniewski, T., Matosiuk, D. and Selent, J., 2018. Protein-Protein Docking in Drug Design and Discovery. Meth- ods in Molecular Biology, pp.285-305. CrossRef

Yesylevskyy, S., Ramseyer, C., Pudlo, M., Pallandre, J. and Borg, C., 2016. Selective Inhibition of STAT3 with Respect to STAT1:Insights from Molecular Dynamics and Ensemble Docking Simulations. Journal of Chemical Information and Modeling, 56(8), pp.1588-1596. CrossRef

Jones, B., 2017. Clinical radiobiology of proton therapy: modeling of RBE. Acta Oncologica, 56(11), pp.1374-1378. CrossRef

Chen, Y. and Ahmad, S., 2011. Empirical model estimation of relative biological effectiveness for proton beam therapy. Radiation Protec- tion Dosimetry, 149(2), pp.116-123. CrossRef

Dahle,, T. J., Rykkelid, A. M., Stokkevåg, C. H., Mairani, A., Görgen, A., Edin, N. J., Rørvik, E., Fjæra, L. F., Malinen, E., & Ytre-Hauge, K.

S. 2017. Monte Carlo simulations of a low energy proton beamline for radiobiological experiments. Acta oncologica (Stockholm, Sweden), 56(6), 779-786. CrossRef

Chen, Y. and Ahmad, S., 2011. Empirical model estimation of relative biological effectiveness for proton beam therapy. Radiation Protec- tion Dosimetry, 149(2), pp.116-123. CrossRef

Hirayama, S., Matsuura, T., Ueda, H., Fujii, Y., Fujii, T., Takao, S., Miyamoto, N., Shimizu, S., Fujimoto, R., Umegaki, K. and Shirato, H., 2018. An analytical dose‐averagedLETcalculation algorithm considering the off‐axisLETenhancement by secondary protons for spot‐scan- ning proton therapy. Medical Physics, 45(7), pp.3404-3416. CrossRef

Bitencourt-Ferreira, G., Pintro, V. and de Azevedo, W., 2019. Docking with AutoDock4. Methods in Molecular Biology, pp.125- 148. CrossRef

Hughes-Oliver, JM, Brooks, AD, Welch, WJ, Khaledi, MG, Hawkins, D, Young, SS, Patil, K, Howell, GW, Ng, RT, Chu, MT, 2012 Chem- ModLab: a web-based cheminformatics modeling laboratory. In Silico Biol, 11(1-2), pp. 61-81.

Morency, L., Gaudreault, F. and Najmanovich, R., 2018. Applications of the NRGsuite and the Molecular Docking Software FlexAID in Com- putational Drug Discovery and Design. Methods in Molecular Biology, pp.367-388. CrossRef

Pirhadi, S., Sunseri, J. and Koes, D., 2016. Open source molecular modeling. Journal of Molecular Graphics and Modelling, 69, pp.127- 143. CrossRef

Vorberg, S. and Tetko, I., 2013. Modeling the Biodegradability of Chemical Compounds Using the Online CHEmical Modeling Environ- ment (OCHEM). Molecular Informatics, 33(1), pp.73-85. CrossRef

Doak, D., Denyer, G., Gerrard, J., Mackay, J. and Allison, J., 2019. Peppy: A virtual reality environment for exploring the principles of polypeptide structure. Protein Science, 29(1), pp.157-168. CrossRef

Letychevskyi, O., Volkov, V., Tarasich, Yu., Sokolova, G., Peschanenko, V, 2022. Modern methods and software systems of molecular mod- elling and application of behavior algebra. Cybernetics and Systems Analysis, 58(3), pp. 150-163. CrossRef

Letychevskyi, O., Volkov, V., Tarasich, Yu., Sokolova, G., Peschanenko, V, 2022. Algebraic Modelling of Molecular Interactions. CCIS Series of Springer, Information and Communication Technologies in Education, Research, and Industrial Applications. 17th International Conference, ICTERI 2021, Kherson, Ukraine, Workshops (In Print)

Letichevsky, A., Letychevskyi, O., Peschanenko, V., 2016. Insertion Modeling and Its Applications. Computer Science Journal of Moldova, 28. 24 (3), pp. 357-370. 2022. APS & IMS. [online] Available at: [Accessed 03 August 2022].

Letichevsky, A. and Gilbert, D., 2000. A Model for Interaction of Agents and Environments. Recent Trends in Algebraic Development Techniques, pp.311-328. CrossRef

Baranov, S., Jervis, C., Kotlyarov, V., Letichevsky, A. and Weigert, T., n.d. Leveraging UML to Deliver Correct Telecom Applications. UML for Real, pp.323-342. CrossRef

Letichevsky, A., Kapitonova, Y., Volkov, V., Letichevsky, A., Baranov, S., Kotlyarov, V. and Weigert, T., 2005. Systems Specification by Basic Protocols. Cybernetics and Systems Analysis, 41(4), pp.479-493.">CrossRef

Letichevsky, A., Godlevsky, A., Letychevsky, A., Potiyenko, S. and Peschanenko, V., 2010. Properties of a predicate transformer of the VRS system. Cybernetics and Systems Analysis, 46(4), pp.521-532.">CrossRef

Letichevsky, A., Kapitonova, J., Letichevsky, A., Volkov, V., Baranov, S. and Weigert, T., 2005. Basic protocols, message sequence charts, and the verification of requirements specifications. Computer Networks, 49(5), pp.661-675. CrossRef



  • There are currently no refbacks.