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RESEARCH ARTICLE

Predictive modeling of drug efficacy in colorectal cancer via a computational strategy integrating structural descriptors and ranking analysis

Shereen Iqbal1† ,  Hifza Iqbal1† ,  Muhammad Kamran2,3∗ ,  Muhammad Akhtar Tarar4† ,  Dragan Pamucar5* ,  Muhammad Farman3,6,7†
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1 Department of Mathematics and Statistics, Faculty of Sciences, The University of Lahore, Lahore, Punjab, Pakistan
2 Department of Mathematics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
3 Mathematics Research Center, Near East University, Nicosia, Turkey
4 Department of Civil Engineering, Faculty of Engineering and Technology, The University of Lahore, Punjab, Pakistan
5 Sustainability Competence Centre, Széchenyi István University, Győr, Hungary
6 Jadara University Research Center, Jadara University, Irbid, Jordan
7 Research Center of Applied Mathematics, Khazar University, Baku, Azerbaijan
†These authors contributed equally to this work.
IJOCTA, 025410175 https://doi.org/10.36922/IJOCTA025410175
Received: 11 October 2025 | Revised: 3 December 2025 | Accepted: 10 December 2025 | Published online: 25 March 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Colorectal cancer is challenging to treat because many anticancer drugs do not achieve optimal therapeutic effects. Moreover, these drugs can cause systemic side effects, and patients often respond differently to treatment. This study provides a computational framework that merges quantitative structure property relationship analysis with a computational methodology combining structural descriptors and ranking analysis to systematically analyze and rank 10 United States Food and Drug Administration approved medicines for colorectal cancer. A suite of degree-based and neighborhood degree-based topological indices were generated and examined for their association with essential physicochemical properties, specially molecular weight and molecular complexity. Correlation research revealed that specific degree-based indices, and neighborhood degree-based indices, exhibited strong predictive power for these properties. By employing ratio weighting alongside with the VIekriterijumsko Kompromisno Rangiranje and Technique for Order Preference by Similarity to Ideal Solution decision techniques, the medicines were ranked based on their predicted physicochemical performance. The results from both decision methods consistently showed fluorouracil as the highest ranked therapeutic agent, followed by tipiracil hydrochloride and bevacizumab, underlining their favorable structural and pharmacological properties. This comprehensive modelling technique provides a consistent and systematic strategy to aid in early-phase drug screening and inform decision-making in colorectal cancer therapy.

Graphical abstract
Keywords
Graph theory
Drug networks
Decision-making
Optimization
Funding
None.
Conflict of interest
The authors declare they have no competing interests
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
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