Respiratory syncytial virus (RSV) is a major pathogen responsible for severe lower respiratory tract infections in infants, the elderly, and immunocompromised individuals. Because the RSV F protein mediates viral entry and 15-lipoxygenase (15-LOX) amplifies virus-induced inflammatory responses, dual targeting of these proteins may provide both antiviral and anti-inflammatory benefits. In this study, we combined computational prediction with experimental validation to identify natural dual-target inhibitors from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). A total of 13,131 natural compounds were screened by drug-likeness evaluation, molecular docking, ADME assessment, and molecular dynamics simulations, yielding 31 potential dual-target candidates with favorable drug-like properties. Among them, rhoeadine (MOL001473) maintained stable binding conformations with both targets throughout 100 ns simulations. In BEAS-2B cells, rhoeadine exhibited significant anti-RSV activity (EC50 = 1.82 µM), low cytotoxicity (IC50 = 34.50 µM), and a selectivity index (SI) of 18.97. Time-of-addition experiments suggested that rhoeadine primarily acts at the early stage of viral infection. Additionally, ELISA results indicated that rhoeadine significantly inhibited RSV-induced secretion of CCL5 and IL-6, highlighting its anti-inflammatory potential. In summary, this study identified rhoeadine as a promising natural compound with antiviral and anti-inflammatory activities against RSV. Computational analyses suggested its potential association with RSV F protein and 15-LOX, although direct target-level validation is still required.

Molecular dynamics (MD) simulation is a computational technique based on Newtonian mechanics and statistical physics that tracks molecular motion and ligand-target recognition at atomic resolution. In recent years, with advances in computational resources and continuous optimization of simulation algorithms, the application of MD simulations in traditional Chinese medicine (TCM) research and development has expanded steadily. Because TCM involves chemically diverse constituents, multiple targets, metabolic transformation, and pathway-level regulation, its mechanisms are difficult to interpret using static or single-target approaches alone. Owing to its high spatiotemporal resolution, MD simulation can characterize binding modes, structural adaptation, and interaction patterns of representative TCM constituents with target proteins. When combined with network pharmacology, multi-omics, artificial intelligence (AI)-assisted modeling, and experimental validation, MD-derived evidence can help transform static compound–target associations into testable mechanistic hypotheses. This review summarizes the methodological development, software platforms, databases, and computational workflows of MD simulation, with emphasis on their relevance to TCM-oriented mechanism research. Representative applications involving flavonoids, alkaloids, glycosides, terpenoids, and polysaccharides are discussed to show how MD provides molecular-level evidence for selected compound-target interactions, membrane-associated behavior, receptor recognition, and free-energy profiles. The review also highlights key challenges, including high computational cost, insufficient sampling, uncertain force-field parameters, difficulty in modeling multicomponent systems, limited trajectory reproducibility, and insufficient experimental validation. Looking ahead, the further integration of artificial intelligence, network pharmacology, and high-throughput screening is exp ected to enhance the role of MD simulations in the modernization of TCM research.