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.

Enterocytozoon bieneusi is a microsporidian pathogen. Recently, the equestrian population is increasing in Korea. The horse-related zoonotic pathogens, including E. bieneusi, are concerns of public health. A total of 1,200 horse fecal samples were collected from riding centers and breeding farms in Jeju Island and inland areas. Of the fecal samples 15 (1.3%) were PCR positive for E. bieneusi. Interestingly, all positive samples came from Jeju Island. Diarrhea and infection in foals were related. Two genotypes (horse1, horse2) were identified as possible zoonotic groups requiring continuous monitoring.

Enterocytozoon bieneusi is an important microsporidian protozoa that causes intestinal disorders in humans. We collected 191 fecal samples from roadkill deer carcasses, among which 13 (6.8%) showed positive reaction for E. bieneusi by polymerase chain reaction assay. Phylogenetic analysis revealed 6 distinct genotypes, 1 of which was novel. All genotypes belonged to Group 1, which has low host specificity, indicating possible transmission through sylvatic cycle. E. bieneusi infection was predominant in female deer (p<0.05).

Enterocytozoon bieneusi is an important microsporidian protozoa that causes intestinal disorders in humans. We collected 191 fecal samples from roadkill deer carcasses, among which 13 (6.8%) showed positive reaction for E. bieneusi by polymerase chain reaction assay. Phylogenetic analysis revealed 6 distinct genotypes, 1 of which was novel. All genotypes belonged to Group 1, which has low host specificity, indicating possible transmission through sylvatic cycle. E. bieneusi infection was predominant in female deer (p<0.05).

Enterocytozoon bieneusi is an important microsporidian protozoa that causes intestinal disorders in humans. We collected 191 fecal samples from roadkill deer carcasses, among which 13 (6.8%) showed positive reaction for E. bieneusi by polymerase chain reaction assay. Phylogenetic analysis revealed 6 distinct genotypes, 1 of which was novel. All genotypes belonged to Group 1, which has low host specificity, indicating possible transmission through sylvatic cycle. E. bieneusi infection was predominant in female deer (p<0.05).

Enterocytozoon bieneusi is an important microsporidian protozoa that causes intestinal disorders in humans. We collected 191 fecal samples from roadkill deer carcasses, among which 13 (6.8%) showed positive reaction for E. bieneusi by polymerase chain reaction assay. Phylogenetic analysis revealed 6 distinct genotypes, 1 of which was novel. All genotypes belonged to Group 1, which has low host specificity, indicating possible transmission through sylvatic cycle. E. bieneusi infection was predominant in female deer (p<0.05).