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MPNN- Based Method for Identifying the Pharmacological Activity of a Synthesized Chemical Compound

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Creativity in Intelligent Technologies and Data Science (CIT&DS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1909))

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Abstract

The paper describes the stages of developing a deep learning model- based method for identifying the pharmacological activity of a synthesized chemical compound. The implemented software is designed to prepare data for training, and testing, using a deep-learning neural network MPNN, obtaining the results of the neural network in the form of a concentration coefficient of half-maximal inhibition. The approaches and technologies used to solve the problems of predicting the activity of a synthesized substance are disclosed.

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Author information

Authors and Affiliations

  1. Volgograd State Technical University, 28 Lenin Av., Volgograd, 400005, Russia

    Alla G. Kravets & Dmitry Gorbatenko

  2. Dubna State University, 19 Universitetskaya St., Dubna, Moscow Region, 141982, Russia

    Alla G. Kravets

  3. Volgograd Institute of Management – branch of the Russian Presidential Academy of National Economy and Public Administration, 8 Gagarin St., Volgograd, 400131, Russia

    Natalia Salnikova & Elizaveta Smolova

  4. Volgograd State University, 100 Universitetskiy Av., Volgograd, 400062, Russia

    Svyatoslav Birukov

Authors
  1. Alla G. Kravets
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  2. Dmitry Gorbatenko
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  3. Natalia Salnikova
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  4. Svyatoslav Birukov
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  5. Elizaveta Smolova
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Corresponding author

Correspondence to Alla G. Kravets .

Editor information

Editors and Affiliations

  1. Volgograd State Technical University, Volgograd, Russia

    Alla G. Kravets

  2. Volgograd State Technical University, Volgograd, Russia

    Maxim V. Shcherbakov

  3. University of Patras, Patras, Greece

    Peter P. Groumpos

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Kravets, A.G., Gorbatenko, D., Salnikova, N., Birukov, S., Smolova, E. (2023). MPNN- Based Method for Identifying the Pharmacological Activity of a Synthesized Chemical Compound. In: Kravets, A.G., Shcherbakov, M.V., Groumpos, P.P. (eds) Creativity in Intelligent Technologies and Data Science. CIT&DS 2023. Communications in Computer and Information Science, vol 1909. Springer, Cham. https://doi.org/10.1007/978-3-031-44615-3_4

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  • DOI: https://doi.org/10.1007/978-3-031-44615-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44614-6

  • Online ISBN: 978-3-031-44615-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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