Aims: Employing the technique of liquid chromatography-mass spectrometry (LCMS) in conjunction with artificial intelligence (AI) technology to predict and screen for antirheumatoid arthritis (RA) active compounds in Xanthocerais lignum.
Background: Natural products have become an important source of new drug discovery. RA is a chronic autoimmune disease characterized by joint inflammation and systemic inflammation. Although there are many drugs available for the treatment of RA, they still have many side effects and limitations. Therefore, finding more effective and safer natural products for the treatment of RA has become an important issue.
Methods: In this study, a collection of inhibitors targeting RA-related specific targets was gathered. Machine learning models and deep learning models were constructed using these inhibitors. The performance of the models was evaluated using a test set and ten-fold cross-validation, and the most optimal model was selected for integration. A total of five commonly used machine learning algorithms (logistic regression, k-nearest neighbors, support vector machines, random forest, XGBoost) and one deep learning algorithm (GCN) were employed in this research. Subsequently, a Xanthocerais lignum compound library was established through HPLC-Q-Exactive- MS analysis and relevant literature. The integrated model was utilized to predict and screen for anti-RA active compounds in Xanthocerais lignum.
Results: The integrated model exhibited an AUC greater than 0.94 for all target datasets, demonstrating improved stability and accuracy compared to individual models. This enhancement enables better activity prediction for unknown compounds. By employing the integrated model, the activity of 69 identified compounds in Xanthocerais lignum was predicted. The results indicated that isorhamnetin-3-O-glucoside, myricetin, rutinum, cinnamtannin B1, and dihydromyricetin exhibited inhibitory effects on multiple targets. Furthermore, myricetin and dihydromyricetin were found to have relatively higher relative abundances in Xanthocerais lignum, suggesting that they may serve as the primary active components contributing to its anti-RA effects.
Conclusion: In this study, we utilized AI technology to learn from a large number of compounds and predict the activity of natural products from Xanthocerais lignum on specific targets. By combining AI technology and the LC-MS approach, rapid screening and prediction of the activity of natural products based on specific targets can be achieved, significantly enhancing the efficiency of discovering new bioactive molecules from medicinal plants.
Keywords: LC-MS; Xanthocerais lignum; active natural products.; artificial intelligence; machine learning; rheumatoid arthritis.
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