Machine learning methods to predict the cultivation age of Panacis Quinquefolii Radix

Metabolomics approach to growth-age discrimination in mountain-cultivated ginseng (Panax ginseng C. A. Meyer) using ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry

Mountain-cultivated ginseng is typically harvested after 10 years, while ginseng aged over 15 years is considered wild ginseng. This study aims to differentiate mountain-cultivated ginseng by age, as the fraudulent practice of selling low-aged cultivated ginseng disguised as high-aged one is damaging the market. In this study, LC-MS analyzed 98 ginseng samples, and multivariate statistical analysis identified patterns between samples to select influential components. Machine learning models were developed to identify ginseng samples of different ages. The untargeted metabolomic analysis clearly divided samples aged 4-20 years into three age groups. Twenty-two potential age-dependent biomarkers were discovered to differentiate the three sample groups. Three machine learning models were used to predict new samples, and the optimal model was selected. Some biomarkers could determine age phases according to the differentiation of mountain-cultivated ginseng samples. These biomarkers were thoroughly analyzed for variation trends. The machine learning models established using the screened biomarkers successfully predicted the age group of new samples.

Identification of intrinsic hepatotoxic compounds in Polygonum multiflorum Thunb. using machine-learning methods

ETHNOPHARMACOLOGICAL RELEVANCE

Polygonum multiflorum Thunb. (PM) is a herb, extracts of which have been used as Chinese medicine for years. Although it is believed to be beneficial to the liver, heart, and kidneys, it causes idiosyncratic drug-induced liver injury (DILI).

AIM OF THE STUDY

We propose that the intrinsic DILI caused by natural products in PM (NPPM) is an important complementary mechanism to PM-related herb-induced liver injury, and aim to identify the ingredients with high DILI potential by machine learning methods.

MATERIALS AND METHODS

One hundred and ninety-seven NPPM were collected from the literature to identify the intrinsic hepatotoxic compounds. Additionally, a DILI-labeled dataset consisting of 2384 compounds was collected and randomly split into training and test sets. A diparametric optimization method was developed to tune the parameters of extended-connectivity fingerprints (ECFPs), Rdkit, and atom-pair fingerprints as well as those of machine-learning (ML) algorithms. Subsequently, K means were employed to cluster the NPPM that were predicted to have a high DILI risk. An in vitro cell-viability assay was performed using HepaRG cells to validate the prediction results.

RESULTS

ECFPs with the top 35% of features ranked by the F-value with support vector machine (SVM) yielded the best performance. The optimized SVM model achieved an accuracy of 0.761 and recall value of 0.834 on the test dataset. The silico screening for NPPM resulted in 47 ingredients with high DILI potential, which were clustered into six groups based on the elbow method. A representative subgroup that contained 21 ingredients, of which two dianthrones exhibited the lowest IC₅₀ value (0.7-0.9 μM) and anthraquinones showed moderate toxicity (15-25 μM), was constructed.

CONCLUSION

Using ML methods and in vitro screening, two classes of compounds, dianthrones and anthraquinones, were predicted and validated to have a high risk of DILI. The diparametric optimization method used in this study could provide a useful and powerful tool to screen toxicants for large datasets and is available at https://github.com/dreadlesss/Hepatotoxicity_predictor.