Integrating experimental findings and machine learning models to predict the anticancer potential of newly...

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 (R²). 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^-1 (equatorial C-2), alongside the characteristic 840 cm^-1 peak, verified site-specific sulfonation. In SFU-1, new peaks at 1243 cm^-1 (SO stretching) and 583 cm^-1 (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 R² (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.