An AI-driven strategy for active compounds discovery and non-destructive quality control in traditional Chinese medicine: A case of Xuefu Zhuyu Oral Liquid

The modernization and globalization of traditional Chinese medicine (TCM) face challenges such as unclear active compounds and inadequate quality control. Taking Xuefu Zhuyu Oral Liquid (XZOL) as an example, this study proposed an artificial intelligence (AI) -driven strategy for active compounds discovery and non-destructive quality control. Firstly, the multi-wavelength fusion high-performance liquid chromatography (HPLC) fingerprints were constructed to comprehensively characterize the chemical composition of XZOL. Secondly, the pro-angiogenesis effects of XZOL were evaluated in a PTK787-induced intersegmental vessels (ISVs) injury zebrafish model. Then, spectrum-effect relationship models, incorporating gray relational analysis (GRA), partial least squares regression (PLSR), backpropagation artificial neural networks (BP-ANN), and convolutional neural networks (CNN), discovered seven pro-angiogenesis active compounds (Hydroxysafflor Yellow A, Paeoniflorin, Ferulic Acid, Narirutin, Naringin, Hesperidin, and Neohesperidin). Furthermore, the efficacy of these compounds was further validated through network pharmacology, molecular docking, and zebrafish. Finally, a rapid and non-destructive quality control system based on near infrared spectroscopy (NIRS) was established. This system effectively distinguished expired and normal samples by combining Hotelling T 2 and Distance to Model X (DModX) statistics of multivariate statistical process control (MSPC), and accurately predicted the content of above active compounds by CNN model integration with bidirectional long short-term memory (Bi-LSTM) and multi-head self-attention (MHSA) networks. This study underscores the potential of AI-driven strategy to enhance TCM standardization and global recognition by providing an active compounds-based holistic quality control strategy of TCM.