ArticleScientific reports2025
Predicting bone cancer drugs properties through topological indices and machine learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Developing a QSPR model for Alzheimer's drugs using topological indices and M-polynomial: A computational study.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chemical graph theory and topological indices are key tools in the study of molecular structures and their properties. This research explores anticancer drugs using neighborhood degree-based topological indices and compares their efficacy through regression and machine learning models. The QSPR approach is applied to 15 anticancer drugs by constructing neighborhood-based molecular graphs, and calculating their respective topological indices. Regression models like quadratic, cubic, and random forest are employed to predict response metrics including like boiling point, refractivity, and surface area of the drugs. Comparative studies indicate that quadratic models provide better predictive performance then their cubic counterparts in most scenarios. Random forest models also demonstrate satisfactory accuracy with smaller error bounds. The present findings highlight the usefulness of topological indices in chemoinformatics and their application in predicting drug response.
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What Socratic 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 Socratic graph.