Drug-likeness analysis of traditional Chinese medicines: 1. property distributions of drug-like compounds, non-drug-like compounds and natural compounds from traditional Chinese medicines

Drug/nondrug classification using Support Vector Machines with various feature selection strategies

In conjunction with the advance in computer technology, virtual screening of small molecules has been started to use in drug discovery. Since there are thousands of compounds in early-phase of drug discovery, a fast classification method, which can distinguish between active and inactive molecules, can be used for screening large compound collections. In this study, we used Support Vector Machines (SVM) for this type of classification task. SVM is a powerful classification tool that is becoming increasingly popular in various machine-learning applications. The data sets consist of 631 compounds for training set and 216 compounds for a separate test set. In data pre-processing step, the Pearson's correlation coefficient used as a filter to eliminate redundant features. After application of the correlation filter, a single SVM has been applied to this reduced data set. Moreover, we have investigated the performance of SVM with different feature selection strategies, including SVM-Recursive Feature Elimination, Wrapper Method and Subset Selection. All feature selection methods generally represent better performance than a single SVM while Subset Selection outperforms other feature selection methods. We have tested SVM as a classification tool in a real-life drug discovery problem and our results revealed that it could be a useful method for classification task in early-phase of drug discovery.

Traditional Chinese herbs as chemical resource library for drug discovery of anti-infective and anti-inflammatory

ETHNOPHARMACOLOGICAL RELEVANCE

Infection 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 METHODS

In 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.

RESULTS

The 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.

CONCLUSION

Traditional 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.

Virtual screening of traditional Chinese medicine (TCM) database: identification of fragment-like lead molecules for filariasis target asparaginyl-tRNA synthetase

Lymphatic filariasis (LF) is a vector borne infectious disease caused by the nematode Wuchereria bancrofti, Brugia malayi, and Brugia timori. Over 120 million people are affected by LF in the world, of which two-thirds are in Asia. The infection restricts the normal flow of lymph from the infected area resulting in swelling of the extremities and causing permanent disability. As the available drugs for the treatment of LF are becoming ineffective due to the development of resistance, there is an urgent need to find new leads for drug development. In this study, asparaginyl-tRNA synthetase (AsnRS; PDB ID: 2XGT) essential for the protein bio-synthesis in the filarial nematode was used to carry out virtual screening (VS) of plant constituents from traditional Chinese medicine (TCM) database. Docking as well as E-pharmacophore based VS were carried out to identify the hits. The top scoring hits, Agri 1 (1,3,8-trihydroxy-4,5-dimethoxyxanthen-9-one-3-O-beta-D-glucopyranoside) and Agri 2 (5,7-dihydroxy-2-propylchromone 7-O-beta-D-glucopyranoside), constituents of Agrimonia pilosa, were selected for molecular dynamics (MD) simulation study for 10 ns. MD simulation showed that both the glycosides Agri 1 and Agri 2 were forming stable interactions with the target protein. Moreover, docking and MD simulation of the lead A (1,3,8-trihydroxy-4,5-dimethoxyxanthen-9-one; Mol. Wt.: 304.25; CLogP: 3.07) and lead B (5,7-dihydroxy-2-propylchromone; Mol. Wt.: 220.22; CLogP: 3.02), the aglycones of Agri 1 and Agri 2, respectively, were carried out with the target AsnRS. The in silico investigations of the aglycones suggest that the lead B could be a suitable fragment-like lead molecule for anti-filarial drug discovery.

TCMSP: a database of systems pharmacology for drug discovery from herbal medicines

Background

Modern medicine often clashes with traditional medicine such as Chinese herbal medicine because of the little understanding of the underlying mechanisms of action of the herbs. In an effort to promote integration of both sides and to accelerate the drug discovery from herbal medicines, an efficient systems pharmacology platform that represents ideal information convergence of pharmacochemistry, ADME properties, drug-likeness, drug targets, associated diseases and interaction networks, are urgently needed.

Description

The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) was built based on the framework of systems pharmacology for herbal medicines. It consists of all the 499 Chinese herbs registered in the Chinese pharmacopoeia with 29,384 ingredients, 3,311 targets and 837 associated diseases. Twelve important ADME-related properties like human oral bioavailability, half-life, drug-likeness, Caco-2 permeability, blood-brain barrier and Lipinski’s rule of five are provided for drug screening and evaluation. TCMSP also provides drug targets and diseases of each active compound, which can automatically establish the compound-target and target-disease networks that let users view and analyze the drug action mechanisms. It is designed to fuel the development of herbal medicines and to promote integration of modern medicine and traditional medicine for drug discovery and development.

Conclusions

The particular strengths of TCMSP are the composition of the large number of herbal entries, and the ability to identify drug-target networks and drug-disease networks, which will help revealing the mechanisms of action of Chinese herbs, uncovering the nature of TCM theory and developing new herb-oriented drugs. TCMSP is freely available at http://sm.nwsuaf.edu.cn/lsp/tcmsp.php.

Identification of novel compounds for human bitter taste receptors

The finely tuned bitter taste sensing in humans is orchestrated by a group of 25 bitter taste receptors (TAS2Rs), which belong to the G-protein-coupled receptor superfamily. TAS2Rs are expressed in the specialized taste bud cells of the gustatory system and perceive a plethora of bitter substances with versatile structures. To date, more than one hundred bitter ligands have been matched with their cognate receptors, but the understanding of the molecular mechanisms of TAS2Rs remains limited. Additionally, the extraoral expression of TAS2R genes was found in the gastrointestinal tract and respiratory system, which suggests other important physiological functions for TAS2Rs. To gain insight into the physiological functions of TAS2Rs, we established a heterologous expression system and characterized the response of 24 TAS2Rs against a library of potential bitter compounds. Among these bitter compounds of interest, 18 bitter compounds activated 16 TAS2Rs, representing 42 tastant-receptor pairs. We then calculated 14 descriptor properties for the 18 positive compounds. By comparison with 102 previously annotated bitter compounds in the database, we discovered common descriptor properties that may contribute to the discovery of additional bitter ligands and further expand the known molecular receptive ranges of human TAS2Rs.

A Drug-Target Network-Based Approach to Evaluate the Efficacy of Medicinal Plants for Type II Diabetes Mellitus

The use of plants as natural medicines in the treatment of type II diabetes mellitus (T2DM) has long been of special interest. In this work, we developed a docking score-weighted prediction model based on drug-target network to evaluate the efficacy of medicinal plants for T2DM. High throughput virtual screening from chemical library of natural products was adopted to calculate the binding affinity between natural products contained in medicinal plants and 33 T2DM-related proteins. The drug-target network was constructed according to the strength of the binding affinity if the molecular docking score satisfied the threshold. By linking the medicinal plant with T2DM through drug-target network, the model can predict the efficacy of natural products and medicinal plant for T2DM. Eighteen thousand nine hundred ninety-nine natural products and 1669 medicinal plants were predicted to be potentially bioactive.

Drug-likeness analysis of traditional Chinese medicines: 2. Characterization of scaffold architectures for drug-like compounds, non-drug-like compounds, and natural compounds from traditional Chinese medicines

Background

In order to better understand the structural features of natural compounds from traditional Chinese medicines, the scaffold architectures of drug-like compounds in MACCS-II Drug Data Report (MDDR), non-drug-like compounds in Available Chemical Directory (ACD), and natural compounds in Traditional Chinese Medicine Compound Database (TCMCD) were explored and compared.

Results

First, the different scaffolds were extracted from ACD, MDDR and TCMCD by using three scaffold representations, including Murcko frameworks, Scaffold Tree, and ring systems with different complexity and side chains. Then, by examining the accumulative frequency of the scaffolds in each dataset, we observed that the Level 1 scaffolds of the Scaffold Tree offer advantages over the other scaffold architectures to represent the scaffold diversity of the compound libraries. By comparing the similarity of the scaffold architectures presented in MDDR, ACD and TCMCD, structural overlaps were observed not only between MDDR and TCMCD but also between MDDR and ACD. Finally, Tree Maps were used to cluster the Level 1 scaffolds of the Scaffold Tree and visualize the scaffold space of the three datasets.

Conclusion

The analysis of the scaffold architectures of MDDR, ACD and TCMCD shows that, on average, drug-like molecules in MDDR have the highest diversity while natural compounds in TCMCD have the highest complexity. According to the Tree Maps, it can be observed that the Level 1 scaffolds present in MDDR have higher diversity than those presented in TCMCD and ACD. However, some representative scaffolds in MDDR with high frequency show structural similarities to those in TCMCD and ACD, suggesting that some scaffolds in TCMCD and ACD may be potentially drug-like fragments for fragment-based and de novo drug design.