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Licorice is a popular sweetener and a thirst quencher in many food products particularly in Europe and the Middle East and also one of the oldest and most frequently used herbs in traditional Chinese medicine. As a wide application of food additive, it is necessary to clarify bioactive chemical ingredients and the mechanism of action of licorice. In this study, a network pharmacology approach that integrated drug-likeness evaluation, structural similarity analysis, target identification, network analysis, and KEGG pathway analysis was established to elucidate the potential molecular mechanism of licorice. First, we collected and evaluated structural information of 282 compounds in licorice and found 181 compounds that met oral drug rules. Then, structural similarity analysis with known ligands of targets in the ChEMBL database (similarity threshold = 0.8) was applied to the initial target identification, which found 63 compounds in licorice had 86 multi-targets. Further, molecular docking was performed to study their binding modes and interactions, which screened out 49 targets. Finally, 17 enriched KEGG pathways (p < 0.01) of licorice were obtained, exhibiting a variety of biological activities. Overall, this study provided a feasible and accurate approach to explore the safe and effective application of licorice as a food additive and herb medicine.

Activating Liver X receptors (LXRs) represents a promising therapeutic option for dyslipidemia. However, activating LXRα may cause undesired lipogenic effects. Discovery of highly LXRβ-selective agonists without LXRα activation were indispensable for dyslipidemia. In this study, in silico approaches were applied to develop highly potent LXRβ-selective agonists based on a series of newly reported 3-(4-(2-propylphenoxy)butyl)imidazolidine-2,4-dione-based LXRα/β dual agonists. Initially, Kohonen and stepwise multiple linear regression SW-MLR were performed to construct models for LXRβ agonists and LXRα agonists based on the structural characteristics of LXRα/β dual agonists, respectively. The obtained LXRβ agonist model gave a good predictive ability (R2train = 0.837, R2test = 0.843, Q2LOO = 0.715), and the LXRα agonist model produced even better predictive ability (R2train = 0.968, R2test = 0.914, Q2LOO = 0.895). Also, the two QSAR models were independent and can well distinguish LXRβ and LXRα activity. Then, compounds in the ZINC database met the lower limit of structural similarity of 0.7, compared to the 3-(4-(2-propylphenoxy)butyl)imidazolidine-2,4-dione scaffold subjected to our QSAR models, which resulted in the discovery of ZINC55084484 with an LXRβ prediction value of pEC50 equal to 7.343 and LXRα prediction value of pEC50 equal to −1.901. Consequently, nine newly designed compounds were proposed as highly LXRβ-selective agonists based on ZINC55084484 and molecular docking, of which LXRβ prediction values almost exceeded 8 and LXRα prediction values were below 0.

In this study, in silico approaches, including multiple QSAR modeling, structural similarity analysis, and molecular docking, were applied to develop QSAR classification models as a fast screening tool for identifying highly-potent ABCA1 up-regulators targeting LXRβ based on a series of new flavonoids. Initially, four modeling approaches, including linear discriminant analysis, support vector machine, radial basis function neural network, and classification and regression trees, were applied to construct different QSAR classification models. The statistics results indicated that these four kinds of QSAR models were powerful tools for screening highly potent ABCA1 up-regulators. Then, a consensus QSAR model was developed by combining the predictions from these four models. To discover new ABCA1 up-regulators at maximum accuracy, the compounds in the ZINC database that fulfilled the requirement of structural similarity of 0.7 compared to known potent ABCA1 up-regulator were subjected to the consensus QSAR model, which led to the discovery of 50 compounds. Finally, they were docked into the LXRβ binding site to understand their role in up-regulating ABCA1 expression. The excellent binding modes and docking scores of 10 hit compounds suggested they were highly-potent ABCA1 up-regulators targeting LXRβ. Overall, this study provided an effective strategy to discover highly potent ABCA1 up-regulators.