https://doi.org/10.1140/epjp/s13360-022-03469-x
Regular Article
Targeting highly resisted anticancer drugs through topological descriptors using VIKOR multi-criteria decision analysis
1
School of Software, Pingdingshan University, 467000, Pingdingshan, China
2
Henan International Joint Laboratory for Multidimensional Topology and Carcinogenic Characteristics Analysis of Atmospheric Particulate Matter PM2.5, 467000, Pingdingshan, China
3
Department of Natural Sciences and Humanities, University of Engineering and Technology, Lahore (RCET), Pakistan
4
Department of Mathematics, Lahore College for Women University, Lahore, Pakistan
Received:
10
September
2022
Accepted:
6
November
2022
Published online:
15
November
2022
The disease cancer is expanding on high spans in virtually all over the world, and undoubtedly, the research through all the aspects of sciences for each of its perspective is a great cause in reducing its severeness and symptoms. Chemotherapy is itself a cure to cancer as it helps in controlling the formation of cancerous cells but leaving multiple side effects on a human body. In this research work, we targeted 21 anticancer drugs that are in taken by the patients in combinations during chemotherapies. We introduce another branch of mathematics named as OR (Operations Research) linking to the chemical graph theory. Chemical graph theory allows us to generate highly resistant research on any structure via quantitative structure property relationship (QSPR) modeling to explore and develop new compounds for drugs. In this research study, we visualized what else the QSPR could provide when it comes to ranking drugs. We visualized the results obtained for boiling points and enthalpy of vaporizations through QSPR as the values of correlation coefficients and the errors generated under unique QSPR modeling. The implementation of VIKOR provides the best ranking for each of anticancer drugs when keeping in concern the specified properties, and the conclusions from this research work show another path to biologist scientists to create best combinations keeping in concern the study generated from QSPR.
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