IntroductionCOVID-19 is a respiratory tract infection caused by the SARS-CoV-2 virus, which has a considerable impact on human central nervous system, digestive system, respiratory system and other systems. Since its discovery in 2019, it has triggered a worldwide pandemic and brought great disaster to people around the world1. To date, more than 770 million people worldwide have suffered from COVID-19, and more than 7 million people have died according to World Trade Organization (WTO) data reports. During the pandemic years, although researchers have been working on COVID-19 treatments and have achieved significant progress, several challenges remain. At present, the main treatment methods for COVID-19 include monoclonal antibodies, neuropathy drugs, targeted drug therapy, immunomodulatory therapy, antifibrotic and anticoagulant therapy, metabolic modulators, recovery from cognitive impairment and other methods2,3. However, drug therapy has limited efficacy, drug resistance, side effects and other problems4. As a key strategy for containing the current pandemic, numerous relevant studies on COVID-19 are well underway and have yielded promising results. Current research falls into two categories: one focuses on specific viral components, while the other examines the whole virus. However, they also have problems such as antibody dependence and even some short-term or long-term harm to the human body5,6. Therefore, there is an urgent need for highly effective drugs with minimal side effects to treat COVID-19.Traditional Chinese medicine (TCM) has demonstrated significant advantages in preventing and controlling COVID-19. According to the guidance of TCM theory and the compatibility of various TCMs, it has a good effect on the symptoms of COVID-19 and other chain clinical symptoms7,8. The Chinese medicinal compound Xiangxue Decoction (XXD) originates from the ancient Chinese text Taiping Huimin Heji Ju Fang and comprises four primary ingredients. Glycyrrhiza uralensis Fisch, Citrus reticulata Blanco, and Cyperus rotundus L., which dredges wind, relieves the surface, disperses cold and relieves pain. Perilla frutescens (L.) Britt contains phenolic compounds, terpenes and other key active components, which have strong antiviral potential9,10. Flavonoids and tannins show antigenic viral activity, and they also have free radical scavenging activity11,12. Flavonoids such as pectin isolated from Citrus reticulata Blanco also have inhibitory effects on SARS-CoV-213. Active ingredients such as liquiritin and liquiritigenin isolated from Glycyrrhiza uralensis Fisch have also been proven to be effective in treating pneumonia and lung injury, and can effectively downregulate inducible nitric oxide synthase (iNOS) levels14,15. However, the interaction mechanism and common effects of several drugs have not been studied in depth, so further studies are needed to clarify the mechanism and explore the therapeutic effect of these drugs on COVID-19.With the continuous advancement of science and technology, network pharmacology has emerged as a key approach for studying compound components and analyzing drug-disease interactions. Network pharmacology has also developed from the original single “one drug-one target-one disease” to the current mode of “multiple targets forming a network and multicomponent therapy“16. TCM also has multicomponent, multitarget and comprehensive efficacy in treatment and diagnosis, so the application of network pharmacology to explore the composition mechanism of TCM compounds has become widely popular17.In recent years, the rapid development of computational (in silico) technologies has provided powerful tools for deciphering the complex systems of multi-component and muti-target18. Network pharmacology, by constructing "component-target-disease" interaction networks, enables systematic prediction of TCM mechanisms of action and potential active ingredients. Building on this foundation, molecular docking and molecular dynamics simulations can visualize and evaluate interaction patterns and biding stability between active ingredients and key target proteins at the atomic level, providing structural biological evidence for network prediction19. Furthermore, machine learning and artificial intelligence models have been employed to enhance the accuracy and efficiency of target prediction. These multi-level and complementary computational approaches have been successfully applied in mechanistic exploration studies of various TCM compound formulations against COVID-19, demonstrating their effectiveness and practicality in this field20.In this study, we employed network pharmacology to identify relevant targets and signaling pathways of the TCM compound Xiangxue Decoction (XXD), establishing a foundation for subsequent research and novel drug development. We further conducted molecular docking analysis and molecular dynamics simulations to assess binding affinities between active compounds and core targets, alongside HPLC-Q-TOF-MS/MS analysis to characterize XXD’s phytochemical composition and bioactive constituents.Fig. 1Full size image(a) Map of drug active ingredient targets. The yellow circle represents the compositions of Glycyrrhiza uralensis Fisch, the green diamond represents the compositions of Citrus reticulata Blanco, the gray V represents the compositions of Cyperus rotundus L., the purple square represents the compositions of Perilla frutescens (L.) Britt, and the orange circle represents the common compositions of four single Chinese medicines (Glycyrrhiza uralensis Fisch, Citrus reticulata Blanco, Cyperus rotundus L. and Perilla frutescens (L.) Britt). The blue square represents the target sites of the XXD. The component with the highest correlation with the target COVID-19 was Citrus reticulata Blanco, and the target with the highest correlation was PTGS2. (b) Venn diagram of XXD targets and COVID-19 targets. There are 162 unique targets for XXD, 1485 unique targets for COVID-19, and 64 intersection targets. (c) The PPI network involving XXD and COVID-19 targets. The circles represent the proteins targeted by COVID-19 and XXD, and the target protein with the highest correlation is TNF-α.Analysis materials and methodsHPLC‒Q-TOF‒MS/MSInstruments and materialsAmong the four herbal components, Citrus reticulata Blanco, Perilla frutescens (L.) Britt and Glycyrrhiza uralensis Fisch were sourced from Oriental National Medicine Co., Ltd. (Jiaxing, Zhejiang, China). Citrus reticulata Blanco was obtained from the Affiliated Hospital of Zhejiang Chinese Medical University (Hangzhou, Zhejiang, China). All botanical materials were authenticated by Associate Professor Jing Chen of the College of Life Sciences, Zhejiang Chinese Medical University. All remaining reagents were analytical grade. Instrumentation included a high-resolution quadrupole time-of-flight liquid chromatography-mass spectrometry system (Waters, USA) and a circulating water multi-purpose vacuum pump (Zhengzhou Great Wall Science, Industry & Trade Co., Ltd., China).Sample preparationBy consulting the records of the ancient text “Taiping Huimin Heji Ju Fang”, we determined that the ratio of the four medicinal materials in the XXD, namely, Cyperus rotundus L.: Perilla frutescens (L.) Britt: Glycyrrhiza uralensis Fisch: Citrus reticulata Blanco, was 4:4:1:2. The drink (110 g) was cut into small sections, 10 volumes of 70% ethanol (1100 mL) was added, and the mixture was heated and refluxed at 80 °C for 1 h. The supernatant was filtered and collected, and 8 volumes of 70% ethanol (880 mL) was added to the residue. The supernatants were combined, concentrated in a rotary evaporator, concentrated into two samples with high concentrations (100 mg/mL) and low concentrations (10 mg/mL), and stored in a refrigerator at 4 °C.Chromatographic conditionsThe column was a Waters SYNAPT G2-Si column (2.1 mm * 100 mm, 1.6 μm); the flow rate was 0.3 ml/min; the column temperature was 35 °C; the sample chamber temperature was 10 °C; the sample size was 2 µl; the mobile phase was 0.1% formic acid water and pure acetonitrile; and gradient elution was performed for 35 min (0–2 min, 5% acetonitrile; 2–32 min, 5–100% acetonitrile; 32–33 min, 100% acetonitrile; 33.5 min, 5% acetonitrile; 33.5–35 min, 5% acetonitrile).Mass spectrometry conditionsDuring the data acquisition process, both positive and negative electrospray ionization (ESI) modes were employed. This dual-mode scanning strategy is essential because complex mixtures contain compounds with diverse physicochemical properties. Some components are more efficiently ionized through protonation in the positive ion mode ([M + H]+), while others compounds perform better through deprotonation in the negative ion mode ([M-H]−). Data acquisition in both modes ensures a more comprehensive and unbiased analysis of the chemical composition of XXD. Full-scan mode was used, the scan time was 0.2 s, and the scan range was 50-1200. The collision energy used was MSE, with a low collision energy of 6 V and a high collision energy of 15–45 V. Mass spectrometer correction was performed via sodium formate, and real-time quality correction was performed for leucine enkephalin (positive ion mode, 556.2771 m/z; negative ion mode, 554.2615 m/z). The capillary voltage was as follows: positive ion, 3 kV; negative ion, 2.5 kV; sample cone, 40 V; source offset, 80 V; source temperature, 120 °C; desolvation temperature, 500 °C; negative ion, 400 °C; desolvation gas, 1000 L/h; negative ion, 800 L/h; nebulizer, 6.5 bar21.Chemical composition analysisTwo samples with different concentrations were analyzed according to the chromatography and mass spectrometry conditions. With information on the peaks of the compounds, the main chemical components of the samples were identified. Through screening, the components with high responses at different concentrations and good matching degrees in the samples were selected22.Identification and verification of compoundsThe UNIFI software was first employed to perform automatic matching based on precise mass numbers and the TCM database, which the mass error window for primary mass spectrometry was set to 5 ppm, generating preliminary results. Subsequently, all preliminary matching results undergo validation according to the following criteria: (1) Precise mass: In the primary mass spectrum (MS1), the absolute error between the measured mass number of the precursor ion and the theoretical mass number should be