ArticleCurrent organic synthesis2026
Ranking Antidiabetic Drugs Using a Multi-criteria Decision-making Approach Based on Domination Distance-based Topological Indices and QSPR Modeling.
Article in Current organic synthesis, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionDiabetes is a rapidly increasing metabolic disorder influenced by lifestyle and diet. Therefore, identifying effective therapeutic agents is of great importance. Chemical graph theory, through topological indices, helps relate molecular structures to physicochemical and thermodynamic properties. However, the application of domination distance-based topological indices (DDTIs) for quantitative structure-property relationship (QSPR) modeling and the ranking of antidiabetic drugs remains largely unexplored. This study investigates the relationships between DDTIs and the physicochemical properties of antidiabetic drugs using a QSPR model and ranks the drugs based on these indices integrated with multi-criteria decision-making (MCDM) methods.
methodsA QSPR approach is employed using DDTIs. Cubic regression is applied to model the relationships between these indices and key physicochemical properties. To identify the most promising drug candidates, MCDM methods, namely, the technique for order preference by similarity to ideal solution (TOPSIS), weighted sum method (WSM), and weighted product method (WPM), are applied based on the calculated DDTIs.
resultsStrong correlations are observed between the DDTIs and the selected physicochemical properties, enabling the development of effective predictive models. Eighteen antidiabetic drugs are ranked using TOPSIS, WSM, and WPM, integrated with DDTIs, with high consistency among the rankings, demonstrating the robustness of the approach. DISCUSSION: The utility of domination distance-based indices in predicting drug properties and the effectiveness of MCDM methods in drug prioritization is highlighted. While the results align with previous QSPR studies, further validation with larger datasets is recommended.
conclusionThe findings demonstrate the predictive potential of DDTIs and the effectiveness of MCDM methods for drug prioritization. This framework enables the prediction and ranking of antidiabetic drugs, aiding the discovery of effective therapeutic candidates.
Indexed as
Identifiers
42768480What OpenQuestion holds
Registered trials
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.