Oxidative stress is a well-established risk factor for numerous chronic diseases, emphasizing the need for efficient identification of potent antioxidants.Conventional methods for assessing antioxidant properties are often time-consuming and resource-intensive, typically relying on laborious biochemical assays. In this study, we investigated the applicability of machine learning (ML) algorithms for predicting the antioxidant activity of compounds based solely on their molecular structure. We evaluated the performance of five ML algorithms, Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Random Forest (RF), and Deep Neural Network (DNN), using a dataset of over 1,900 compounds with experimentally determined antioxidant activity. Both RF and SVM achieved the best overall performance, exhibiting high accuracy (> 0.9) and effectively distinguishing active and inactive compounds with high structural similarity. External validation using natural product data from the BATMAN database confirmed the generalizability of the RF and SVM models. Our results suggest that ML models serve as powerful tools to expedite the discovery of novel antioxidant candidates, potentially streamlining the development of future therapeutic interventions.

Oxidative stress is a well-established risk factor for numerous chronic diseases, emphasizing the need for efficient identification of potent antioxidants.Conventional methods for assessing antioxidant properties are often time-consuming and resource-intensive, typically relying on laborious biochemical assays. In this study, we investigated the applicability of machine learning (ML) algorithms for predicting the antioxidant activity of compounds based solely on their molecular structure. We evaluated the performance of five ML algorithms, Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Random Forest (RF), and Deep Neural Network (DNN), using a dataset of over 1,900 compounds with experimentally determined antioxidant activity. Both RF and SVM achieved the best overall performance, exhibiting high accuracy (> 0.9) and effectively distinguishing active and inactive compounds with high structural similarity. External validation using natural product data from the BATMAN database confirmed the generalizability of the RF and SVM models. Our results suggest that ML models serve as powerful tools to expedite the discovery of novel antioxidant candidates, potentially streamlining the development of future therapeutic interventions.

Oxidative stress is a well-established risk factor for numerous chronic diseases, emphasizing the need for efficient identification of potent antioxidants.Conventional methods for assessing antioxidant properties are often time-consuming and resource-intensive, typically relying on laborious biochemical assays. In this study, we investigated the applicability of machine learning (ML) algorithms for predicting the antioxidant activity of compounds based solely on their molecular structure. We evaluated the performance of five ML algorithms, Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Random Forest (RF), and Deep Neural Network (DNN), using a dataset of over 1,900 compounds with experimentally determined antioxidant activity. Both RF and SVM achieved the best overall performance, exhibiting high accuracy (> 0.9) and effectively distinguishing active and inactive compounds with high structural similarity. External validation using natural product data from the BATMAN database confirmed the generalizability of the RF and SVM models. Our results suggest that ML models serve as powerful tools to expedite the discovery of novel antioxidant candidates, potentially streamlining the development of future therapeutic interventions.

Oxidative stress is a well-established risk factor for numerous chronic diseases, emphasizing the need for efficient identification of potent antioxidants.Conventional methods for assessing antioxidant properties are often time-consuming and resource-intensive, typically relying on laborious biochemical assays. In this study, we investigated the applicability of machine learning (ML) algorithms for predicting the antioxidant activity of compounds based solely on their molecular structure. We evaluated the performance of five ML algorithms, Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Random Forest (RF), and Deep Neural Network (DNN), using a dataset of over 1,900 compounds with experimentally determined antioxidant activity. Both RF and SVM achieved the best overall performance, exhibiting high accuracy (> 0.9) and effectively distinguishing active and inactive compounds with high structural similarity. External validation using natural product data from the BATMAN database confirmed the generalizability of the RF and SVM models. Our results suggest that ML models serve as powerful tools to expedite the discovery of novel antioxidant candidates, potentially streamlining the development of future therapeutic interventions.

Oxidative stress is a well-established risk factor for numerous chronic diseases, emphasizing the need for efficient identification of potent antioxidants.Conventional methods for assessing antioxidant properties are often time-consuming and resource-intensive, typically relying on laborious biochemical assays. In this study, we investigated the applicability of machine learning (ML) algorithms for predicting the antioxidant activity of compounds based solely on their molecular structure. We evaluated the performance of five ML algorithms, Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Random Forest (RF), and Deep Neural Network (DNN), using a dataset of over 1,900 compounds with experimentally determined antioxidant activity. Both RF and SVM achieved the best overall performance, exhibiting high accuracy (> 0.9) and effectively distinguishing active and inactive compounds with high structural similarity. External validation using natural product data from the BATMAN database confirmed the generalizability of the RF and SVM models. Our results suggest that ML models serve as powerful tools to expedite the discovery of novel antioxidant candidates, potentially streamlining the development of future therapeutic interventions.