A machine learning (ML)-based framework was developed for predicting and optimizing the antioxidant activity of Ainsliaea acerifolia water extracts. while the response surface methodology (RSM) is deficient in modeling nonlinear interactions. In this study, three machine learning (ML) algorithms, Extreme Gradient Boosting (XGB), Random Forest (RF), and Support Vector Machine (SVM), were evaluated using extraction variables (temperature, time, and solvent-to-solid ratio) along with flavonoid and polyphenol content as input features. Among the models evaluated, the XGB model showed the most advanced antioxidant prediction capabilities, as evidenced by its R² of 0.9835 and RMSE of 2.52 on the test data set. The biological significance of the features was explored using SHAP analysis, revealing flavonoid content and extraction temperature as key contributors. A graphical user interface (GUI) was developed to facilitate real-time prediction, enhancing accessibility for researchers and industrial users. This approach improves operational efficiency by optimizing extraction conditions, predicting antioxidant activity from data including flavonoids and polyphenols, and reducing reagent usage. This study highlights the potential of ML as a sustainable alternative for natural product optimization and lays the groundwork for future research that integrates bioactivity prediction with formulation design.
Kim et al. (2025) studied this question.