RESUMEN
Due to the increased concern about energy and environmental issues, significant attention has been paid to the development of large-scale energy storage devices to facilitate the utilization of clean energy sources. The redox flow battery (RFB) is one of the most promising systems. Recently, the high cost of transition-metal complex-based RFB has promoted the development of aqueous RFBs with redox-active organic molecules. To expand the working voltage, computational chemistry has been applied to search for organic molecules with lower or higher redox potentials. However, redox potential computation based on implicit solvation models would be challenging due to difficulty in parametrization when considering the complex solvation of supporting electrolytes. Besides, although ab initio molecular dynamics (AIMD) describes the supporting electrolytes with the same level of electronic structure theory as the redox couple, the application is impeded by the high computation costs. Recently, machine learning molecular dynamics (MLMD) has been illustrated to accelerate AIMD by several orders of magnitude without sacrificing the accuracy. It has been established that redox potentials can be computed by MLMD with two separated machine learning potentials (MLPs) for reactant and product states, which is redundant and inefficient. In this work, an automated workflow is developed to construct a universal MLP for both states, which can compute the redox potentials or acidity constants of redox-active organic molecules more efficiently. Furthermore, the predicted redox potentials can be evaluated at the hybrid functional level with much lower costs, which would facilitate the design of aqueous organic RFBs.
RESUMEN
Gouty arthritis (GA) is a common metabolic rheumatological disease. Si-Miao decoction has therapeutic effects on GA. In our study, we investigated the mechanism of Si-Miao decoction against GA using network pharmacology and molecular docking analytical methods. The Traditional Chinese Medicine Systems Pharmacology Database was used as the basis for screening the main targets and agents of the Si-Miao decoction, and the Genecards, OMIM, and Drugbank databases were used to screen GA-related targets. They were analyzed using Venn with the drug targets to obtain the intersection targets. We used Cytoscape 3.9.1 to draw the "Drugs-Compounds-Targets" network and the String database for creative protein-protein interaction networks of target genes and filtered core targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used to analyze the core targets. Molecular docking was performed using AutoDockTools to predict the binding capacity between nuclear targets and active components in the Si-Miao decoction. A total of 50 chemically active components containing 53 common targets of Si-Miao decoction anti-GA and 53 potential drug target proteins were identified. Core targets, namely, TNF, STAT3, SRC, PPARG, TLR4, PTGS2, MMP9, RELA, TGFB1, and SIRT1, were obtained through PPI network analysis. GO and KEGG analyses showed that the mechanism of anti-GA in Si-Miao decoction may proceed by regulating biological processes such as inflammatory factor levels, cell proliferation, apoptosis, and lipid and glucose metabolism, and modulating the NOD-like receptor signaling pathway, IL-17 signaling pathway, TNF signaling pathway, NF-kappa B signaling pathway, and Toll-like receptor signaling pathway. We further screened the core targets, including PTGS2, MMP9, and PPAGR, as receptor proteins based on their degree value and molecular docking with the main active compounds in Si-Miao decoction, and found that baicalein had high affinity. In conclusion, Si-Miao decoction, through anti-inflammatory, apoptosis-regulating, and anti-oxidative stress action mechanisms in the treatment of GA.