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| Format: | Artículo científico |
| Sprache: | en |
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Computational biology and chemistry
2026
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| Online-Zugang: | https://pubmed.ncbi.nlm.nih.gov/41950759/ |
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| author | Kocabay, Samet Memiş, Samet Taşkın, Irmak İçen Sever, Meryem Rüveyda Şener, Ramazan |
| author_facet | Kocabay, Samet Memiş, Samet Taşkın, Irmak İçen Sever, Meryem Rüveyda Şener, Ramazan Kocabay, Samet Memiş, Samet Taşkın, Irmak İçen Sever, Meryem Rüveyda Şener, Ramazan |
| collection | PubMed - marine biology |
| contents | Integrating experimental findings and machine learning models to predict the anticancer potential of newly synthesized oversulfated fucoidan. Kocabay, Samet Memiş, Samet Taşkın, Irmak İçen Sever, Meryem Rüveyda Şener, Ramazan Polysaccharides Humans Machine Learning Antineoplastic Agents Drug Screening Assays, Antitumor Cell Line, Tumor Cell Proliferation Developing effective cancer treatments requires identifying novel therapeutic agents with high biological activity. Fucoidan, a sulfated polysaccharide from brown algae, is a promising natural scaffold for anticancer drug development. In this study, oversulfated fucoidans (SFU and SFU-1) were derived from natural fucoidans (FU and FU-1), and the effects of this structural modification on anticancer efficacy were investigated comprehensively. Chemical characterizations of FU/FU-1 and SFU/SFU-1 were performed, and a systematically generated experimental dataset across multiple cancer cell lines was compiled. Using these data, several machine learning (ML) algorithms were applied to predict the anticancer efficacy of fucoidan-based molecules. Specifically, k-Nearest Neighbors Regression (kNNR), Least-Squares Boosting (LSBoost), Support Vector Regression (SVR), Decision Tree Regression (DT), Random Forest Regression (RFR), Linear Regression (LR), and Gaussian Process Regression (GPR) were evaluated with five-fold cross-validation. Model performance was assessed utilizing the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Coefficient of Determination (R). The results show that FT-IR analysis of the oversulfated derivatives, SFU and SFU-1, confirmed successful modification. For SFU, the appearance of a shoulder peak at 820 cm (equatorial C-2), alongside the characteristic 840 cm peak, verified site-specific sulfonation. In SFU-1, new peaks at 1243 cm (SO stretching) and 583 cm (OSO deformations) were identified. These spectral changes demonstrate the effective integration of sulfate groups into the molecular frameworks. Cytotoxicity assays against five human cancer cell lines revealed dose-dependent inhibition, with the SFU derivative exhibiting the most potent activity, particularly reducing HeLa and MDA-MB-231 cell viability to 23.93% and 25.13% at 2 mg/mL. GPR model achieves superior predictive performance compared to other methods, with the lowest MAE and RMSE (8.4627 and 11.5692, respectively) and the highest R (0.7039) values. The findings reveal that models that capture the nonlinear relationship between sulfation degree and anticancer efficacy, especially GPR, are powerful tools for the preliminary evaluation of natural product-based drug candidates. This study demonstrates that integrating chemical modification, experimental validation, and classical ML can accelerate the rational assessment of naturally derived therapeutics in oncology. |
| format | Artículo científico |
| id | pubmed_41950759 |
| institution | PubMed |
| language | en |
| publishDate | 2026 |
| publisher | Computational biology and chemistry |
| record_format | pubmed |
| spellingShingle | Integrating experimental findings and machine learning models to predict the anticancer potential of newly synthesized oversulfated fucoidan. Kocabay, Samet Memiş, Samet Taşkın, Irmak İçen Sever, Meryem Rüveyda Şener, Ramazan Polysaccharides Humans Machine Learning Antineoplastic Agents Drug Screening Assays, Antitumor Cell Line, Tumor Cell Proliferation Integrating experimental findings and machine learning models to predict the anticancer potential of newly synthesized oversulfated fucoidan. Kocabay, Samet Memiş, Samet Taşkın, Irmak İçen Sever, Meryem Rüveyda Şener, Ramazan Polysaccharides Humans Machine Learning Antineoplastic Agents Drug Screening Assays, Antitumor Cell Line, Tumor Cell Proliferation Developing effective cancer treatments requires identifying novel therapeutic agents with high biological activity. Fucoidan, a sulfated polysaccharide from brown algae, is a promising natural scaffold for anticancer drug development. In this study, oversulfated fucoidans (SFU and SFU-1) were derived from natural fucoidans (FU and FU-1), and the effects of this structural modification on anticancer efficacy were investigated comprehensively. Chemical characterizations of FU/FU-1 and SFU/SFU-1 were performed, and a systematically generated experimental dataset across multiple cancer cell lines was compiled. Using these data, several machine learning (ML) algorithms were applied to predict the anticancer efficacy of fucoidan-based molecules. Specifically, k-Nearest Neighbors Regression (kNNR), Least-Squares Boosting (LSBoost), Support Vector Regression (SVR), Decision Tree Regression (DT), Random Forest Regression (RFR), Linear Regression (LR), and Gaussian Process Regression (GPR) were evaluated with five-fold cross-validation. Model performance was assessed utilizing the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Coefficient of Determination (R). The results show that FT-IR analysis of the oversulfated derivatives, SFU and SFU-1, confirmed successful modification. For SFU, the appearance of a shoulder peak at 820 cm (equatorial C-2), alongside the characteristic 840 cm peak, verified site-specific sulfonation. In SFU-1, new peaks at 1243 cm (SO stretching) and 583 cm (OSO deformations) were identified. These spectral changes demonstrate the effective integration of sulfate groups into the molecular frameworks. Cytotoxicity assays against five human cancer cell lines revealed dose-dependent inhibition, with the SFU derivative exhibiting the most potent activity, particularly reducing HeLa and MDA-MB-231 cell viability to 23.93% and 25.13% at 2 mg/mL. GPR model achieves superior predictive performance compared to other methods, with the lowest MAE and RMSE (8.4627 and 11.5692, respectively) and the highest R (0.7039) values. The findings reveal that models that capture the nonlinear relationship between sulfation degree and anticancer efficacy, especially GPR, are powerful tools for the preliminary evaluation of natural product-based drug candidates. This study demonstrates that integrating chemical modification, experimental validation, and classical ML can accelerate the rational assessment of naturally derived therapeutics in oncology. |
| title | Integrating experimental findings and machine learning models to predict the anticancer potential of newly synthesized oversulfated fucoidan. |
| topic | Polysaccharides Humans Machine Learning Antineoplastic Agents Drug Screening Assays, Antitumor Cell Line, Tumor Cell Proliferation |
| url | https://pubmed.ncbi.nlm.nih.gov/41950759/ |