ReviewPloS one2025
Innovative approaches in QSPR modelling using topological indices for the development of cancer treatments.
Review in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Integrating Machine Learning Approaches and Topological Indices for Prediction of Electronic Properties in Anticancer Drugs.ACS omega · 2026Article
- Prediction of properties of some drugs used in the treatment of bipolar disorder via various Zagreb indices.Scientific reports · 2026Article
- Graph-Theoretical Approach for Predicting Physicochemical Properties of Stiff-Person Syndrome Drugs.ChemistryOpen · 2026Article
- Machine Learning Techniques for Computational Characterization of Protein-Associated Small Molecules with Therapeutic Relevance.ACS omega · 2026Article
- QSPR modelling of PPI drugs using hybrid topological indices.Frontiers in chemistry · 2026Article
- Eccentricity-based topological indices of QSPR modelling using anti-cancer drugs.Frontiers in chemistry · 2026Article
- Developing a QSPR model for Alzheimer's drugs using topological indices and M-polynomial: A computational study.Scientific reports · 2025Article
- Predicting bone cancer drugs properties through topological indices and machine learning.Scientific reports · 2025Article
- Quantitative structure-property relationship modeling and ranking of necrotizing fasciitis drugs via degree-based topological indices.Frontiers in chemistry · 2025Article
- QSPR graph model to explore physicochemical properties of potential antiviral drugs of dengue disease through novel coloring-based topological indices.Frontiers in chemistry · 2025Article
- M-polynomial driven machine learning models for predicting physicochemical properties of antibiotics.PloS one · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper provides a comprehensive review of quantitative structure-property relationships (QSPR) about to cancer drugs, with a focus on the application of topological indices (TI) and data analysis techniques. Cancer is a serious and life-threatening disease for which no complete cure currently exists. Consequently, extensive research is ongoing to develop new therapeutic agents. The application of topological indices in chemistry and medicine, particularly in the investigation of the molecular, pharmacological, and therapeutic properties of drugs, has become a significant tool. This article investigates the potential of Temperature indices in analyzing the physicochemical properties of drugs used for cancer treatment. The approach employs QSPR modeling to establish correlations between the molecular structure of a compound and its physical and chemical properties. The analysis covers a range of Cancer drugs, including Aminopterin, Convolutamide A, Convolutamydine A, Daunorubicin, Minocycline, Podophyllotoxin, Caulibugulone E, Perfragilin A, Melatonin, Tambjamine K, Amathaspiramide E, and Aspidostomide E. The findings demonstrate that optimal regression models (Fifty-eight models) incorporating TI can effectively predict physicochemical properties, such as Boiling Point (BP), Enthalpy (EN), Flash Point (FP), Molar Refractivity (MR), Polar Surface Area (PSA), Surface Tension (ST), Molecular Volume (MV), and Complexity (COM). This research suggests that temperature-based topological indices (TI) are promising tools for the development and optimization of cancer drugs, as demonstrated by statistically significant results with a p-value less than 0.05. In addition to the linear regression model, which performed the best, two other machine learning models, namely SVR and Random Forest, were also used for further analysis and comparison of their performance in predicting the physicochemical properties of drugs, to assess the advantages and disadvantages of each model.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.