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September 10, 2025Scientific ReportsOpen Access

Predicting bone cancer drugs properties through topological indices and machine learning

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Research demonstrates accuracy of regression models in predicting properties of anticancer drugs, suggesting potential in chemoinformatics.

Topological indices significantly enhance predictions of drug properties through regression techniques.
Quadratic models outperform cubic counterparts for better accuracy in drug property prediction.
Random forest models provide satisfactory accuracy with smaller error bounds across various metrics.
Comparative analysis highlights the effectiveness of neighborhood degree-based indices in QSPR studies.

Ahmed et al. (2025) studied this question.

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September 10, 2025Scientific ReportsOpen Access

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Authors

Research demonstrates accuracy of regression models in predicting properties of anticancer drugs, suggesting potential in chemoinformatics.

Topological indices significantly enhance predictions of drug properties through regression techniques.
Quadratic models outperform cubic counterparts for better accuracy in drug property prediction.
Random forest models provide satisfactory accuracy with smaller error bounds across various metrics.
Comparative analysis highlights the effectiveness of neighborhood degree-based indices in QSPR studies.

Ahmed et al. (2025) studied this question.

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