Inflammation is a protective biological response to body/tissue damage that involves immune cells, blood vessels and molecular mediators. In this work, we constructed the pathway network of inflammation, including 11 sub-pathways of inflammatory factors. Pathway-based network efficiency and network flux were adopted to evaluate drug efficacy. By using approved and experimentally validated anti-inflammatory drugs as training sets, a predictive model was built to screen potential anti-inflammatory drugs from approved drugs in DrugBank. This drug repositioning approach would bring a fast and cheap way to find new indications for approved drugs. Moreover, molecular phenomics profiles of the expression of inflammatory factors will provide new insight into the drug mechanism of action.

Chemogenomics focuses on the interactions between biologically active molecules and protein targets for drug discovery. Carbohydrates are the most abundant compounds in natural products. Compared with other drugs, the carbohydrate drugs show weaker side effects. Searching for multi-target carbohydrate drugs can be regarded as a solution to improve therapeutic efficacy and safety. In this work, we collected 60344 carbohydrates from the Universal Natural Products Database (UNPD) and explored the chemical space of carbohydrates by principal component analysis. We found that there is a large quantity of potential lead compounds among carbohydrates. Then we explored the potential of carbohydrates in drug discovery by using a network-based multi-target computational approach. All carbohydrates were docked to 2389 target proteins. The most potential carbohydrates for drug discovery and their indications were predicted based on a docking score-weighted prediction model. We also explored the interactions between carbohydrates and target proteins to find the pathological networks, potential drug candidates and new indications.

Cancer is a complex disease, known medically as malignant neoplasm. Natural products (NPs) play a very important role in anticancer drug discovery and a large number of NPs have been proven to have potential anticancer effects. Compared with newly synthesized chemical compounds, NPs show a favorable profile in terms of their absorption and metabolism in the body with low toxicity. Searching for multi-target natural drugs can be regarded as a solution to improve therapeutic efficacy and safety. In this work, we collected 104 cancer-associated target proteins from the Protein Data Bank. Based on the Universal Natural Products Database, all of the NPs were docked to 104 cancer-associated target proteins. Then we explored the potential of NPs and several herbs in anticancer drug discovery by using a network-based multi-target computational approach. The NPs with the most potential for anticancer drug discovery and their indications were predicted based on a docking score-weighted prediction model. We also explored the interactions between NPs and cancer target proteins to find the pathological networks, potential drug candidates and new indications.

Fluoroquinolones play an important role in the treatment of serious bacterial infections, but at the same time they could lead to cardiac toxicity due to the blockage of the HERG potassium channel, which even leads to the withdrawal of some fluoroquinolones. Blockage of the HERG potassium channel by drugs or drug-like compounds has become a critical problem in drug discovery. Though there were large amounts of bioactivity data of fluoroquinolones on the blockage of HERG, little structural basis of binding of blockers to the HERG channel was known. Here, we combined molecular docking, molecular dynamics simulations, free energy calculations and binding energy decomposition analysis to explore the binding modes of fluoroquinolones in the HERG potassium channel. The calculated binding free energies were consistent with the experimental binding affinities. Our results showed that the CH3 group in MX was favorable for the binding to the HERG channel, while Tyr652 and Phe656 were critical for the hydrophobic interaction between fluoroquinolones and the HERG channel. We expected that our results of calculation could provide important insights for the rational design and discovery of drugs.

In this work, we prepared a monolithic and surface initiated molecularly imprinted polymeric (MIP) column for HPLC and explored its application for template separation from plant extract. The silica beads (40–60 μm) were coupled with initiator on the surface and then packed in to a stainless steel HPLC column. The pre-polymerization mixture (the template, functional monomer and crosslinker were emodin, acrylamide and divinylbenzene, respectively) was injected into the column and polymerized by thermal initiation. The prepared MIP column exhibited excellent retention capability and large imprinted factor for template (the retention time and imprinted factor for emodin on MIP column were 16.26 min and 7.21, respectively). Moreover, the emodin-molecularly imprinted polymeric column could be applied to separate emodin from alcohol extract of Rheum palmatum L. at semi-preparative scale and 1.2 mg of emodin was obtained in 4 h.Highlights► A monolithic and surface initiated molecularly imprinted polymeric column for HPLC was prepared by in situ synthesis. ► The emodin-MIP column exhibited excellent retention capability and large imprinted factor for its template. ► The emodin-MIP column could be applied to separate emodin from alcohol extract of Rheum palmatum L. at semi-preparative scale. ► This method used a small quantity of template, which would be important for some natural products if they are difficult to obtain.

Many Traditional Chinese Medicines (TCMs) are effective to relieve complicated diseases such as type II diabetes mellitus (T2DM). In this work, molecular docking and network analysis were employed to elucidate the action mechanism of a medical composition which had clinical efficacy for T2DM. We found that multiple active compounds contained in this medical composition would target multiple proteins related to T2DM and the biological network would be shifted. We predicted the key players in the medical composition and some of them have been reported in literature. Meanwhile, several compounds such as Rheidin A, Rheidin C, Sennoside C, procyanidin C1 and Dihydrobaicalin were notable although no one have reported their pharmacological activity against T2DM. The association between active compounds, target proteins and other diseases was also discussed.Graphical abstractHighlights► The action mechanism of Tangminling Pills which was effective for relieving T2DM was elucidated by virtual screening and network analysis. ► Multiple active compounds would target multiple proteins to influence the whole biological network. ► Five novel compounds which were unknown to be associated with T2DM were highlighted. ► The association between active compounds, target proteins and diseases was discussed. ► Network analysis was powerful to describe the importance of each node.

The interaction between drug molecules and target proteins is the basis of pharmacological action. The pharmacodynamic mechanism of Chinese medicinal plants for chronic kidney disease (CKD) was studied by molecular docking and complex network analysis. It was found that the interaction network of components-proteins of Chinese medicinal plants is different from the interaction network of components-proteins of drugs. The action mechanism of Chinese medicinal plants is different from that of drugs. We also found the interaction network of components-proteins of tonifying herbs is different from the interaction network of components-proteins of evil expelling herbs using complex network research approach. It illuminates the ancient classification theory of Chinese medicinal plants. This computational approach could identify the pivotal components of Chinese medicinal plants and their key target proteins rapidly. The results provide data for development of multi-component Chinese medicine.

Some molecules can form aggregates in solution that may inhibit several enzymes, known as promiscuous inhibitors. This phenomenon has been found in traditional Chinese medicinal recipes. To study the aggregation, the classification model was constructed by the support vector machine (SVM) classifier. The results indicated that this classification model had good stability and good prediction performance. The experimental results showed that this predicted model had good generalization. The model was also used to predict the molecules in the Chinese herbal drugs database (CHDD) for further research.

A facile method to fabricate molecularly imprinted polymers (MIPs) on glass microspheres in a column was developed. The column was prepacked with glass microspheres, and then the prepolymerization mixture was injected into the interstitial volume of the column. The polymerization took place in situ and the column could be directly used for high-performance liquid chromatography after the template had been removed. The template consumption was reduced greatly because the prepolymerization mixture just filled the interstitial volume between the glass microspheres in the column. The MIPs obtained exhibited better kinetic properties, higher efficiency, and low back pressure of the column. Emodin imprinted polymers were prepared by this method and were used for solid-phase extraction.

“Frequent hitter” phenomenon emerged in the high-throughput screening; one of the most common mechanisms behind artifactual inhibition is that some organic molecules formed large colloid-like aggregates which are able to sequester and thereby inhibit enzymes. To investigate the situation in Traditional Chinese Medicine (TCM), 60 medicinal herbs and 24 Chinese herbal formulae were detected by dynamic light scattering (DLS), and aggregates were observed in all the 84 solution mixtures. The aggregates of two Chinese herbal formulae, ‘Xue-Fu-Zhu-Yu Tang’ (XF) and ‘Jing-Guan Tang’ (JG), were not only able to survive in the gastro-intestinal environment, but also had the ability to pass through the monolayer of the Caco-2 cell. The activities of XF and JG against three cardiovascular targets were also aggregates-related. Based on these findings, a new possible mechanism of the action of Chinese medicine was proposed.

Ethnopharmacological relevanceInfection is a major group of diseases which caused significant mortality and morbidity worldwide. Traditional Chinese herbs have been used to treat infective diseases for thousands years. The numerous clinical practices in disease therapy make it a large chemical resource library for drug discovery.Materials and methodsIn this study, we collected 1156 kinds of herbs and 22,172 traditional Chinese medicinal compounds (Tcmcs). The chemical informatics and network pharmacology were employed to analyze the anti-infective effects of herbs and Tcmcs. In order to evaluate the drug likeness of Tcmcs, the molecular descriptors of Tcmcs and FDA-approved drugs were calculated and the chemical space was constructed on the basis of principal component analysis in the eight descriptors. On purpose to estimate the effects of Tcmcs to the targets of FDA-approved anti-infective or anti-inflammatory drugs, the molecular docking was employed. After that, docking score weighted predictive models were used to predict the anti-infective or anti-inflammatory efficacy of herbs.ResultsThe distribution of herbs in the phylogenetic tree showed that most herbs were distributed in family of Asteraceae, Fabaceae and Lamiaceae. Tcmcs were well coincide with drugs in chemical space, which indicated that most Tcmcs had good drug-likeness. The predictive models obtained good specificity and sensitivity with the AUC values above 0.8. At last, 389 kinds of herbs were obtained which were distributed in 100 families, by using the optimal cutoff values in ROC curves. These 389 herbs were widely used in China for treatment of infection and inflammation.ConclusionTraditional Chinese herbs have a considerable number of drug-like natural products and predicted activities to the targets of approved drugs, which would give us an opportunity to use these herbs as a chemical resource library for drug discovery of anti-infective and anti-inflammatory.Download high-res image (337KB)Download full-size image