Proceedings of the 2nd International Conference on Neural Networks and Machine Learning 2023 (ICNNML 2023)

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NMTCM Polynomial Analysis in the Study of Anticancer Drugs

Authors
A. S. Maragadam1, *, Sushmitha Jain2, V. Lokesha1, Dafik3, I. H. Agustin4
1Department of Studies in Mathematics, Vijayanagara Sri Krishnadevaraya University, Ballari, India
2Department of Studies in Mathematics, Ballari Institute of Technology and Management, Ballari, India
3Department of Studies in Mathematics Education, Universitas Jember, Jember, Indonesia
4Department of Studies in Mathematics, Universitas Jember, Jember, Indonesia
*Corresponding author. Email: maragadamvijay@gmail.com
Corresponding Author
A. S. Maragadam
Available Online 29 June 2024.
DOI
10.2991/978-94-6463-445-7_14How to use a DOI?
Keywords
topological indices; topological coindices; M-polynomial; molecular graph; NM-polynomial
Abstract

Topological coindices serve as numerical descriptors that capture the structural intricacies of a molecular graph. These indices offer valuable insights into the spatial arrangement of atoms and bonds within a molecule, shedding light on its connectivity and overall structural relationships. In the context of this research, the focus is on calculating the M-polynomial and NM-polynomial for anticancer drugs. These polynomials are employed as tools to extract specific degree-based topological indices. The study further entails the computation and comparative analysis of NM-polynomials and molecular descriptors for selected anticancer drugs. The investigation delves into the practical and essential exploration of molecular graph structures, particularly those relevant to effective cancer treatment. Additionally, the research establishes a mathematical relationship between the NM-polynomial and NMTCM polynomials.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2nd International Conference on Neural Networks and Machine Learning 2023 (ICNNML 2023)
Series
Advances in Intelligent Systems Research
Publication Date
29 June 2024
ISBN
10.2991/978-94-6463-445-7_14
ISSN
1951-6851
DOI
10.2991/978-94-6463-445-7_14How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - A. S. Maragadam
AU  - Sushmitha Jain
AU  - V. Lokesha
AU  - Dafik
AU  - I. H. Agustin
PY  - 2024
DA  - 2024/06/29
TI  - NMTCM Polynomial Analysis in the Study of Anticancer Drugs
BT  - Proceedings of the 2nd International Conference on Neural Networks and Machine Learning 2023 (ICNNML 2023)
PB  - Atlantis Press
SP  - 118
EP  - 144
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-445-7_14
DO  - 10.2991/978-94-6463-445-7_14
ID  - Maragadam2024
ER  -