CVDHD: a cardiovascular disease herbal database for drug discovery and network pharmacology

Harnessing network pharmacology in drug discovery: an integrated approach

Traditional drug discovery approach is based on one drug-one target, that is associated with very lengthy timelines, high costs and very low success rates. Network pharmacology (NP) is a novel method of drug designing, that is based on a multiple-target approach. NP integrates systems such as biology, pharmacology and computational techniques to address the limitations of traditional methods of drug discovery. With help of mapping biological networks, it provides deep insights into biological molecules' interactions and enhances our understanding to the mechanism of drugs, polypharmacology and disease etiology. This review explores the theoretical framework of network pharmacology, discussing the principles and methodologies that enable the construction of drug-target and disease-gene networks. It highlights how data mining, bioinformatics tools and computational models are utilised to predict drug behaviour, repurpose existing drugs and identify novel therapeutic targets. Applications of network pharmacology in the treatment of complex diseases-such as cancer, neurodegenerative disorders, cardiovascular diseases and infectious diseases-are extensively covered, demonstrating its potential to identify multi-target drugs for multifaceted disease mechanisms. Despite the promising results, NP faces challenges due to incomplete and quality of biological data, computational complexities and biological system redundancy. It also faces regulatory challenges in drug approval, demanding revision in regulatory guidelines towards multi-target therapies. Advancements in AI and machine learning, dynamic network modelling and global collaboration can further enhance the efficacy of network pharmacology. This integrative approach has the potential to revolutionise drug discovery, offering new solutions for personalised medicine, drug repurposing and tackling the complexities of modern diseases.

Artificial intelligence in traditional Chinese medicine: advances in multi-metabolite multi-target interaction modeling

Traditional Chinese Medicine (TCM) utilizes multi-metabolite and multi-target interventions to address complex diseases, providing advantages over single-target therapies. However, the active metabolites, therapeutic targets, and especially the combination mechanisms remain unclear. The integration of advanced data analysis and nonlinear modeling capabilities of artificial intelligence (AI) is driving the transformation of TCM into precision medicine. This review concentrates on the application of AI in TCM target prediction, including multi-omics techniques, TCM-specialized databases, machine learning (ML), deep learning (DL), and cross-modal fusion strategies. It also critically analyzes persistent challenges such as data heterogeneity, limited model interpretability, causal confounding, and insufficient robustness validation in practical applications. To enhance the reliability and scalability of AI in TCM target prediction, future research should prioritize continuous optimization of the AI algorithms using zero-shot learning, end-to-end architectures, and self-supervised contrastive learning.

Overview and limitations of database in global traditional medicines: A narrative review

The study of traditional medicine has garnered significant interest, resulting in various research areas including chemical composition analysis, pharmacological research, clinical application, and quality control. The abundance of available data has made databases increasingly essential for researchers to manage the vast amount of information and explore new drugs. In this article we provide a comprehensive overview and summary of 182 databases that are relevant to traditional medicine research, including 73 databases for chemical component analysis, 70 for pharmacology research, and 39 for clinical application and quality control from published literature (2000-2023). The review categorizes the databases by functionality, offering detailed information on websites and capacities to facilitate easier access. Moreover, this article outlines the primary function of each database, supplemented by case studies to aid in database selection. A practical test was conducted on 68 frequently used databases using keywords and functionalities, resulting in the identification of highlighted databases. This review serves as a reference for traditional medicine researchers to choose appropriate databases and also provides insights and considerations for the function and content design of future databases.

Microtubules and cardiovascular diseases: insights into pathology and therapeutic strategies

Microtubules, complex cytoskeletal structures composed of tubulin proteins in eukaryotic cells, have garnered recent attention in cardiovascular research. Investigations have focused on the post-translational modifications of tubulin, including acetylation and detyrosination. Perturbations in microtubule homeostasis have been implicated in various pathological processes associated with cardiovascular diseases such as heart failure, ischemic heart disease, and arrhythmias. Thus, elucidating the intricate interplay between microtubule dynamics and cardiovascular pathophysiology is imperative for advancing preventive and therapeutic strategies. Several natural compounds have been identified to potentially modulate microtubules, thereby exerting regulatory effects on cardiovascular diseases. This review synthesizes current literature to delineate the roles of microtubules in cardiovascular diseases and assesses the potential of natural compounds in microtubule-targeted therapies.

Tetrahydropalmatine from medicinal plants activates human glucokinase to regulate glucose homeostasis

Many synthetic glucokinase activators (GKAs), modulating glucokinase (GK), an important therapeutic target in diabetes have failed to clear clinical trials. In this study, an in silico structural similarity search with differing scaffolds of reference GKAs have been used to identify derivatives from natural product databases. Ten molecules with good binding score and similar interactions to that in the co-crystallized GK as well good activation against recombinant human GK experimentally were identified. Tetrahydropalmatine, an alkaloid present in formulations and drugs from medicinal plants, has not been explored as an antidiabetic agent and no information regarding its mechanism of action or GK activation exists. Tetrahydropalmatine activates GK with EC50 value of 71.7 ± 17.9 μM while lowering the S0.5 (7.1 mM) and increasing Vmax (9.22 μM/min) as compared to control without activator (S0.5 = 10.37 mM; Vmax = 4.8 μM/min). Kinetic data (α and β values) suggests it to act as mixed, nonessential type activator. Using microscale thermophoresis, Kd values of 3.8 μM suggests a good affinity for GK. In HepG2 cell line, the compound potentiated the uptake of glucose and maintained glucose homeostasis by increasing the expression of GK, glycogen synthase, and insulin receptor genes and lowering the expression of glucokinase regulatory protein (GKRP) and glucagon. Tetrahydropalmatine at low concentrations could elicit a good response by reducing expression of GKRP, increasing expression of GK while also activating it. Thus, it could be used alone or in combination as therapeutic drug as it could effectively modulate GK and alter glucose homeostasis.

Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review

The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.

DISPEL: database for ascertaining the best medicinal plants to cure human diseases

Medicinal plants are anticipated to be one of the most valuable resources for the remedial usage in the treatment of various ailments. The data on key medicinal plants and their therapeutic efficacy against various ailments are quite scattered and not available on a single platform. Moreover, currently there is no means/mechanism of finding the best medicinal plant(s) from numerous plants known to cure any disease. DISPEL (Diseases Plants Eliminate) is a compendium of medicinal plants available across the world that are used to cure infectious as well as non-infectious diseases in humans. The association of a medicinal plant with a disease it cures is hereby referred to as 'medicinal plant-disease cured' linkage. The DISPEL database hosts ∼60 000 'medicinal plant-disease cured' linkages encompassing ∼5500 medicinal plants and ∼1000 diseases. This platform provides interactive and detailed visualization of medicinal plants, diseases and their relations using comprehensible network graph representation. The user has the freedom to search the database by specifying the name of disease(s) as well as the scientific/common name(s) of plant. Each 'medicinal plant-disease cured' relation is scored based on the availability of any medicine/product derived from that medicinal plant, information about active compound(s), knowledge regarding the part of plant that is effective and number of distinct articles/books/websites confirming the effectiveness of the medicinal plant. The user can find the best plant(s) that can be used to cure any desired disease(s). The DISPEL database is the first step towards generating the 'most-effective' combination of plants to cure a disease since it delineates as well as ranks all the therapeutic medicinal plants for that disease. The combination of best medicinal plants can then be used to conduct clinical trials and thus pave the way for their use in clinics for treatment of diseases. Database URL https://compbio.iitr.ac.in/dispel.

Chemo-structural diversity of anti-obesity compound database

Nature plays a major role in the development of new drugs which helps in preventing and treating human diseases. Anti-obesity compound database (AOCD) contains comprehensive information on all published small molecules from natural sources with anti-obesity potential targeting pancreatic lipase (PL), appetite suppressant (AS) and adipogenesis (AD). Presently the database contains 349 compounds isolated from 307 plants, 26 marine and 16 microbial sources. Users can query the AOCD database (https://aocd.swmd.co.in/) in several ways. The database was divided into three datasets (PL, AS and AD) to perform chemoinformatic analysis using Platform for Unified Molecular Analysis (PUMA), which were analyzed based on molecular descriptors, scaffold diversity and structural fingerprint diversity. Chemoinformatics study inferred the PL dataset has the highest diversity of compounds based on the Euclidean distance on molecular properties, scaffold diversity and pairwise similarity on fingerprint diversity. This study would hasten the process of anti-obesity drug discovery.

Multifaceted Pharmacological Potentials of Curcumin, Genistein, and Tanshinone IIA through Proteomic Approaches: An In-Depth Review

Phytochemicals possess various intriguing pharmacological properties against diverse pathological conditions. Extensive studies are on-going to understand the structural/functional properties of phytochemicals as well as the molecular mechanisms of their therapeutic function against various disease conditions. Phytochemicals such as curcumin (Cur), genistein (Gen), and tanshinone-IIA (Tan IIA) have multifaceted therapeutic potentials and various efforts are in progress to understand the molecular dynamics of their function with different tools and technologies. Cur is an active lipophilic polyphenol with pleiotropic function, and it has been shown to possess various intriguing properties including antioxidant, anti-inflammatory, anti-microbial, anticancer, and anti-genotoxic properties besides others beneficial properties. Similarly, Gen (an isoflavone) exhibits a wide range of vital functions including antioxidant, anti-inflammatory, pro-apoptotic, anti-proliferative, anti-angiogenic activities etc. In addition, Tan IIA, a lipophilic compound, possesses antioxidant, anti-angiogenic, anti-inflammatory, anticancer activities, and so on. Over the last few decades, the field of proteomics has garnered great momentum mainly attributed to the recent advancement in mass spectrometry (MS) techniques. It is envisaged that the proteomics technology has considerably contributed to the biomedical research endeavors lately. Interestingly, they have also been explored as a reliable approach to understand the molecular intricacies related to phytochemical-based therapeutic interventions. The present review provides an overview of the proteomics studies performed to unravel the underlying molecular intricacies of various phytochemicals such as Cur, Gen, and Tan IIA. This in-depth study will help the researchers in better understanding of the pharmacological potential of the phytochemicals at the proteomics level. Certainly, this review will be highly instrumental in catalyzing the translational shift from phytochemical-based biomedical research to clinical practice in the near future.

The discovery of multi-target compounds with anti-inflammation activity from traditional Chinese medicine by TCM-target effects relationship spectrum

ETHNOPHARMACOLOGICAL RELEVANCE

Rhei Radix et Rhizoma, Lonicerae Japonicae Flos and Zingiberis Rhizoma was widely used in the treatment of inflammatory disease. The discovery of new multi-target compounds for new drug from the TCM was a possible direction.

AIM OF THE STUDY

Multi-target compounds screening based on polypharmacology was an effective method. As an interdisciplinary field, polypharmacology screen multi-target compounds by various methods. So, a flexible screening framework to avoid the disadvantage of single methods is considered to have great significance.

MATERIALS AND METHODS

The research propose a common framework called Traditional Chinese medicine target-effect relationship spectrum (TCM-TERS). TCM-TERS was constructed based on the pharmacophore and molecular docking models, which provided predicted activity by compounds screening. TCM-TERS merge the results of different models and visualize the targeted activity of each compounds. Then the TCM-TERS were analyzed by the analytic hierarchy process and active components were chosen by the contributing factors. The activity of components was verified on the RAW264.7 by RT-PCR.

RESULTS

This article constructed TCM-TERS of Rhei Radix et Rhizoma, Lonicerae Japonicae Flos and Zingiberis Rhizoma with the COX-2, mPGES-1, 5-LOX and SPLA2-IIA. Seven compounds were chosen with multiple targeted activity based on the TCM-TERS, which showed remarkable activity in RT-PCR.

CONCLUSION

The TCM-TERS was an efficient interdisciplinary method for drug discovery of the TCM, which provide a flexible method to the researcher that can screen specific compounds with multiple screening methods.

phytochemdb: a platform for virtual screening and computer-aided drug designing

The phytochemicals of medicinal plants are regarded as a rich source of diverse chemical spaces that have been used as supplements and alternative medicines in the millennium. Even in this era of combinatorial chemical drugs, phytomedicines account for a large share of the statistics of newly approved drugs. In the field of computational aided and rational drug design, there is an urgent need to develop and build a useful phytochemical database management system with a user-friendly interface that allows proper data storage, retrieval and management. We showed 'phytochemdb', a manually managed database that compiles 525 plants and their corresponding 8093 phytochemicals, aiming to incorporate the activities of phytochemicals from medicinal plants. The database collects molecular formula, three-dimensional/two-dimensional structure, canonical SMILES, molecular weight, no. of heavy atoms, no. of aromatic heavy atoms, fraction Csp3, no. of rotatable bonds, no. of H-bond acceptors, no. of H-bond donors, molar refractivity, topological polar surface area, gastrointestinal absorption, Blood-Brain Barrier (BBB) permeant, P-gp substrate, CYP1A2 inhibitor, CYP2C19 inhibitor, CYP2C9 inhibitor, CYP2D6 inhibitor, CYP3A4 inhibitor, Log Kp, Ghose, Veber, Egan, Muegge, bioavailability scores, pan-assay interference compounds, Brenk, Leadlikeness, synthetic accessibility, iLOGP and Lipinski rule of five with the number of violations for each compound. It provides open contribution functions for the researchers who screen phytochemicals in the laboratory and have released their data. 'phytochemdb' is a comprehensive database that gathers most of the information about medicinal plants in one platform, which is considered to be very beneficial to the work of researchers on medicinal plants. 'phytochemdb' is available for free at https://phytochemdb.com/.

Therapeutic Effects and Safe Uses of Plant-Derived Polyphenolic Compounds in Cardiovascular Diseases: A Review

Polyphenols have long been recognized as health-promoting entities, including beneficial effects on cardiovascular disease, but their reputation has been boosted recently following a number of encouraging clinical studies in multiple chronic pathologies, that seem to validate efficacy. Health benefits of polyphenols have been linked to their well-established powerful antioxidant activity. This review aims to provide comprehensive and up-to-date knowledge on the current therapeutic status of polyphenols having sufficient heed towards the treatment of cardiovascular diseases. Furthermore, data about the safety profile of highly efficacious polyphenols has also been investigated to further enhance their role in cardiac abnormalities. Evidence is presented to support the action of phenolic derivatives against cardiovascular pathologies by following receptors and signaling pathways which ultimately cause changes in endogenous antioxidant, antiplatelet, vasodilatory, and anti-inflammatory activities. In addition, in vitro antioxidant and pre-clinical and clinical experiments on anti-inflammatory as well as immunomodulatory attributes of polyphenols have revealed their role as cardioprotective agents. However, an obvious shortage of in vivo studies related to dose selection and toxicity of polyphenols makes these compounds a suitable target for clinical investigations. Further studies are needed for the development of safe and potent herbal products against cardiovascular diseases. The novelty of this review is to provide comprehensive knowledge on polyphenols safety and their health claims. It will help researchers to identify those moieties which likely exert protective and therapeutic effects towards cardiovascular diseases.

Pharmacoinformatics approaches in the discovery of drug-like antimicrobials of plant origin

Medicinal plants have served as an important source for addressing the ailments of humans and animals alike. The emergence of advanced technologies in the field of drug discovery and development has helped in isolating various bioactive phytochemicals and developing them as drugs. Owing to their significant pharmacological benefits and minimum adverse effects, they not only serve as good candidates for therapeutics themselves but also help in the identification and development of related drug like molecules against various metabolic and infectious diseases. The ever-increasing diversity, severity and incidence of infectious diseases has resulted in an exaggerated mortality and morbidity levels. Geno-proteomic mutations in microbes, irrational prescribing of antibiotics, antimicrobial resistance and human population explosion, all call for continuous efforts to discover and develop alternated therapeutic options against the microbes. This review article describes the pharmacoinformatics tools and methods which are currently used in the discovery of bioactive phytochemicals, thus making the process more efficient and effective. The pharmacological aspects of the drug discovery and development process have also been reviewed with reference to the in silico activities. Communicated by Ramaswamy H. Sarma.

Machine learning approaches for elucidating the biological effects of natural products

Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural products (NPs) has developed in order to manage the challenge of the discovery of bioactive NPs. In the present review, we will introduce the basic principles and protocols for using the ML approach to investigate the bioactivity of NPs, citing a series of practical examples regarding the study of anti-microbial, anti-cancer, and anti-inflammatory NPs, etc. ML algorithms manage a variety of classification and regression problems associated with bioactive NPs, from those that are linear to non-linear and from pure compounds to plant extracts. Inspired by cases reported in the literature and our own experience, a number of key points have been emphasized for reducing modeling errors, including dataset preparation and applicability domain analysis.

Pain Chemogenomics Knowledgebase (Pain-CKB) for Systems Pharmacology Target Mapping and Physiologically Based Pharmacokinetic Modeling Investigation of Opioid Drug-Drug Interactions

More than 50 million adults in America suffer from chronic pain. Opioids are commonly prescribed for their effectiveness in relieving many types of pain. However, excessive prescribing of opioids can lead to abuse, addiction, and death. Non-steroidal anti-inflammatory drugs (NSAIDs), another major class of analgesic, also have many problematic side effects including headache, dizziness, vomiting, diarrhea, nausea, constipation, reduced appetite, and drowsiness. There is an urgent need for the understanding of molecular mechanisms that underlie drug abuse and addiction to aid in the design of new preventive or therapeutic agents for pain management. To facilitate pain related small-molecule signaling pathway studies and the prediction of potential therapeutic target(s) for the treatment of pain, we have constructed a comprehensive platform of a pain domain-specific chemogenomics knowledgebase (Pain-CKB) with integrated data mining computing tools. Our new computing platform describes the chemical molecules, genes, proteins, and signaling pathways involved in pain regulation. Pain-CKB is implemented with a friendly user interface for the prediction of the relevant protein targets and analysis and visualization of the outputs, including HTDocking, TargetHunter, BBB predictor, and Spider Plot. Combining these with other novel tools, we performed three case studies to systematically demonstrate how further studies can be conducted based on the data generated from Pain-CKB and its algorithms and tools. First, systems pharmacology target mapping was carried out for four FDA approved analgesics in order to identify the known target and predict off-target interactions. Subsequently, the target mapping outcomes were applied to build physiologically based pharmacokinetic (PBPK) models for acetaminophen and fentanyl to explore the drug-drug interaction (DDI) between this pair of drugs. Finally, pharmaco-analytics was conducted to explore the detailed interaction pattern of acetaminophen reactive metabolite and its hepatotoxicity target, thioredoxin reductase.

Data-driven microbiota biomarker discovery for personalized drug therapy of cardiovascular disease

Cardiovascular disease (CVD) is the most wide-spread disorder all over the world. The personalized and precision diagnosis, treatment and prevention of CVD is still a challenge. With the developing of metagenome sequencing technologies and the paradigm shifting to data-driven discovery in life science, the computer aided microbiota biomarker discovery for CVD is becoming reality. We here summarize the data resources, knowledgebases and computational models available for CVD microbiota biomarker discovery, and review the present status of the findings about the microbiota patterns associated with the therapeutic effects on CVD. The future challenges and opportunities of the translational informatics on the personalized drug usages in CVD diagnosis, prognosis and treatment are also discussed.

An enumeration of natural products from microbial, marine and terrestrial sources

The discovery of a new drug is a multidisciplinary and very costly task. One of the major steps is the identification of a lead compound, i.e. a compound with a certain degree of potency and that can be chemically modified to improve its activity, metabolic properties, and pharmacokinetics profiles. Terrestrial sources (plants and fungi), microbes and marine organisms are abundant resources for the discovery of new structurally diverse and biologically active compounds. In this chapter, an attempt has been made to quantify the numbers of known published chemical structures (available in chemical databases) from natural sources. Emphasis has been laid on the number of unique compounds, the most abundant compound classes and the distribution of compounds in terrestrial and marine habitats. It was observed, from the recent investigations, that ~500,000 known natural products (NPs) exist in the literature. About 70 % of all NPs come from plants, terpenoids being the most represented compound class (except in bacteria, where amino acids, peptides, and polyketides are the most abundant compound classes). About 2,000 NPs have been co-crystallized in PDB structures.

Applications of Network Pharmacology in Traditional Chinese Medicine Research

Human diseases, especially infectious ones, have been evolving constantly. However, their treatment strategies are not developing quickly. Some diseases are caused by a variety of factors with very complex pathologies, and the use of a single drug cannot solve these problems. Traditional Chinese Medicine (TCM) medication is a unique treatment method in China. TCM formulae contain multiple herbs with multitarget, multichannel, and multilink characteristics. In recent years, with the flourishing development of network pharmacology, a new method for searching therapeutic drugs has emerged. The multitarget action in network pharmacology is consistent with the complex mechanisms of disease and drug action. Using network pharmacology to understand TCM is an emerging trend.

Plant Metabolite Databases: From Herbal Medicines to Modern Drug Discovery

Traditional herbal medicine has been an inseparable part of the traditional medical science in many countries throughout history. Nowadays, the popularity of using herbal medicines in daily life, as well as clinical practices, has gradually expanded to numerous Western countries with positive impacts and acceptance. The continuous growth of the herbal consumption market has promoted standardization and modernization of herbal-derived products with present pharmacological criteria. To store and extensively share this knowledge with the community and serve scientific research, various herbal metabolite databases have been developed with diverse focuses under the support of modern advances. The advent of these databases has contributed to accelerating research on pharmaceuticals of natural origins. In the scope of this study, we critically review 30 herbal metabolite databases, discuss different related perspectives, and provide a comparative analysis of 18 accessible noncommercial ones. We hope to provide you with fundamental information and multidimensional perspectives from herbal medicines to modern drug discovery.

Systems Pharmacology for Investigation of the Mechanisms of Action of Traditional Chinese Medicine in Drug Discovery

As a traditional medical intervention in Asia and a complementary and alternative medicine in western countries, traditional Chinese medicine (TCM) has attracted global attention in the life science field. TCM provides extensive natural resources for medicinal compounds, and these resources are generally regarded as effective and safe for use in drug discovery. However, owing to the complexity of compounds and their related multiple targets of TCM, it remains difficult to dissect the mechanisms of action of herbal medicines at a holistic level. To solve the issue, in the review, we proposed a novel approach of systems pharmacology to identify the bioactive compounds, predict their related targets, and illustrate the molecular mechanisms of action of TCM. With a predominant focus on the mechanisms of actions of TCM, we also highlighted the application of the systems pharmacology approach for the prediction of drug combination and dynamic analysis, the synergistic effects of TCMs, formula dissection, and theory analysis. In summary, the systems pharmacology method contributes to understand the complex interactions among biological systems, drugs, and complex diseases from a network perspective. Consequently, systems pharmacology provides a novel approach to promote drug discovery in a precise manner and a systems level, thus facilitating the modernization of TCM.

YaTCM: Yet another Traditional Chinese Medicine Database for Drug Discovery

Traditional Chinese Medicine (TCM) has a long history of widespread clinical applications, especially in East Asia, and is becoming frequently used in Western countries. However, owing to extreme complicacy in both chemical ingredients and mechanism of action, a deep understanding of TCM is still difficult. To accelerate the modernization and popularization of TCM, a single comprehensive database is required, containing a wealth of TCM-related information and equipped with complete analytical tools. Here we present YaTCM (Yet another Traditional Chinese Medicine database), a free web-based toolkit, which provides comprehensive TCM information and is furnished with analysis tools. YaTCM allows a user to (1) identify the potential ingredients that are crucial to TCM herbs through similarity search and substructure search, (2) investigate the mechanism of action for TCM or prescription through pathway analysis and network pharmacology analysis, (3) predict potential targets for TCM molecules by multi-voting chemical similarity ensemble approach, and (4) explore functionally similar herb pairs. All these functions can lead to one systematic network for visualization of TCM recipes, herbs, ingredients, definite or putative protein targets, pathways, and diseases. This web service would help in uncovering the mechanism of action of TCM, revealing the essence of TCM theory and then promoting the drug discovery process. YaTCM is freely available at http://cadd.pharmacy.nankai.edu.cn/yatcm/home.

IMPPAT: A curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics

Phytochemicals of medicinal plants encompass a diverse chemical space for drug discovery. India is rich with a flora of indigenous medicinal plants that have been used for centuries in traditional Indian medicine to treat human maladies. A comprehensive online database on the phytochemistry of Indian medicinal plants will enable computational approaches towards natural product based drug discovery. In this direction, we present, IMPPAT, a manually curated database of 1742 Indian Medicinal Plants, 9596 Phytochemicals, And 1124 Therapeutic uses spanning 27074 plant-phytochemical associations and 11514 plant-therapeutic associations. Notably, the curation effort led to a non-redundant in silico library of 9596 phytochemicals with standard chemical identifiers and structure information. Using cheminformatic approaches, we have computed the physicochemical, ADMET (absorption, distribution, metabolism, excretion, toxicity) and drug-likeliness properties of the IMPPAT phytochemicals. We show that the stereochemical complexity and shape complexity of IMPPAT phytochemicals differ from libraries of commercial compounds or diversity-oriented synthesis compounds while being similar to other libraries of natural products. Within IMPPAT, we have filtered a subset of 960 potential druggable phytochemicals, of which majority have no significant similarity to existing FDA approved drugs, and thus, rendering them as good candidates for prospective drugs. IMPPAT database is openly accessible at: https://cb.imsc.res.in/imppat.

TCMAnalyzer: A Chemo- and Bioinformatics Web Service for Analyzing Traditional Chinese Medicine

Traditional Chinese medicine (TCM) has been widely used and proven effective in long term clinical practice. However, the molecular mechanism of action for many TCMs remains unclear due to the complexity of many ingredients and their interactions with biological receptors. This is one of the major roadblocks in TCM modernization. In order to solve this problem, we have developed TCMAnalyzer, which is a free web-based toolkit allowing a user to (1) identify the potential compounds that are responsible for the bioactivities for a TCM herb through scaffold-activity relation searches using structural search techniques, (2) investigate the molecular mechanism of action for a TCM herb at the systemic level, and (3) explore the potentially targeted bioactive herbs. The toolkit can result in TCM networks that demonstrate the relations among natural product molecules (small molecular ligands), putative protein targets, pathways, and diseases. These networks are graphically depicted to reveal the mechanism of actions for a TCM herb or to identify new molecular scaffolds for new chemotherapies. TCMAnalyzer is freely available at http://www.rcdd.org.cn/tcmanalyzer .

How Can Synergism of Traditional Medicines Benefit from Network Pharmacology?

Many prescriptions of traditional medicines (TMs), whose efficacy has been tested in clinical practice, have great therapeutic value and represent an excellent resource for drug discovery. Research into single compounds of TMs, such as artemisinin from Artemisia annua L., has achieved great success; however, it has become evident that a TM prescription (which frequently contains various herbs or other components) has a synergistic effect in effecting a cure or reducing toxicity. Network pharmacology targets biological networks and analyzes the links among drugs, targets, and diseases in those networks. Comprehensive, systematic research into network pharmacology is consistent with the perspective of holisticity, which is a main characteristic of many TMs. By means of network pharmacology, research has demonstrated that many a TM show a synergistic effect by acting at different levels on multiple targets and pathways. This approach effectively bridges the gap between modern medicine and TM, and it greatly facilitates studies into the synergistic actions of TMs. There are different kinds of synergistic effects with TMs, such as synergy among herbs, effective parts, and pure compounds; however, for various reasons, new drug discovery should at present focus on synergy among pure compounds.

Network Pharmacology Studies on the Bioactive Compounds and Action Mechanisms of Natural Products for the Treatment of Diabetes Mellitus: A Review

Diabetes mellitus (DM) is a kind of chronic and metabolic disease, which can cause a number of diseases and severe complications. Network pharmacology approach is introduced to study DM, which can combine the drugs, target proteins and disease and form drug-target-disease networks. Network pharmacology has been widely used in the studies of the bioactive compounds and action mechanisms of natural products for the treatment of DM due to the multi-components, multi-targets, and lower side effects. This review provides a balanced and comprehensive summary on network pharmacology from current studies, highlighting different bioactive constituents, related databases and applications in the investigations on the treatment of DM especially type 2. The mechanisms related to type 2 DM, including α-amylase and α-glucosidase inhibitory, targeting β cell dysfunction, AMPK signal pathway and PI3K/Akt signal pathway are summarized and critiqued. It suggests that the network pharmacology approach cannot only provide a new research paradigm for natural products, but also improve the current antidiabetic drug discovery strategies. Furthermore, we put forward the perspectives on the reasonable applications of network pharmacology for the therapy of DM and related drug discovery.

Network pharmacology of cancer: From understanding of complex interactomes to the design of multi-target specific therapeutics from nature

Despite massive investments in drug research and development, the significant decline in the number of new drugs approved or translated to clinical use raises the question, whether single targeted drug discovery is the right approach. To combat complex systemic diseases that harbour robust biological networks such as cancer, single target intervention is proved to be ineffective. In such cases, network pharmacology approaches are highly useful, because they differ from conventional drug discovery by addressing the ability of drugs to target numerous proteins or networks involved in a disease. Pleiotropic natural products are one of the promising strategies due to their multi-targeting and due to lower side effects. In this review, we discuss the application of network pharmacology for cancer drug discovery. We provide an overview of the current state of knowledge on network pharmacology, focus on different technical approaches and implications for cancer therapy (e.g. polypharmacology and synthetic lethality), and illustrate the therapeutic potential with selected examples green tea polyphenolics, Eleutherococcus senticosus, Rhodiola rosea, and Schisandra chinensis). Finally, we present future perspectives on their plausible applications for diagnosis and therapy of cancer.

Pharmacodynamics and potential synergistic effects of Mai-Luo-Ning injection on cardiovascular protection, based on molecular docking

As a computer-assisted approach, molecular docking has been universally applied in drug research and development and plays an important role in the investigation and evaluation of herbal medicines. Herein, the method was used to estimate the pharmacodynamics of Mai-Luo-Ning injection, a traditional Chinese compound herbal prescription. Through investigating the interactions between several important proteins in cardiovascular system and characteristic components of the formula, its effect on cardiovascular protection was evaluated. Results showed the differences in the interactions between each component and the selected target proteins and revealed the possible mechanisms for synergistic effects of various characteristic components on cardiovascular protection. The study provided scientific evidence supporting the mechanistic study of the interactions among multi-components and targets, offering a general approach to investigating the pharmacodynamics of complicated materials in compound herbal prescriptions.

Review of natural product databases

OBJECTIVES

Many natural products have pharmacological or biological activities that can be of therapeutic benefit in treating diseases, and are also an important source of inspiration for development of potential novel drugs. The past few decades have witnessed extensive study of natural products for their promising prospects in application of medicinal chemistry, molecular biology and pharmaceutical sciences.

MATERIALS AND METHODS

Natural product databases have provided systematic collection of information concerning natural products and their derivatives, including structure, source and mechanisms of action, which significantly support modern drug discovery.

RESULTS

Currently, a considerable number of natural product databases, such as TCM Database@Taiwan, TCMID, CEMTDD, SuperToxic and SuperNatural, have been developed, providing data such as integrated medicinal herbs, ingredients, 2D/3D structures of the compounds, related target proteins, relevant diseases, and metabolic toxicity and more.

CONCLUSIONS

We focus on an analytical overview of current natural product databases, and further discuss the good, bad or imperfection of current ones, in the hope of better integrating existing relevant outcomes, thus providing new routes for future 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.

AlzPlatform: an Alzheimer's disease domain-specific chemogenomics knowledgebase for polypharmacology and target identification research

Alzheimer’s disease (AD) is one of the most complicated progressive neurodegeneration diseases that involve many genes, proteins, and their complex interactions. No effective medicines or treatments are available yet to stop or reverse the progression of the disease due to its polygenic nature. To facilitate discovery of new AD drugs and better understand the AD neurosignaling pathways involved, we have constructed an Alzheimer’s disease domain-specific chemogenomics knowledgebase, AlzPlatform (www.cbligand.org/AD/) with cloud computing and sourcing functions. AlzPlatform is implemented with powerful computational algorithms, including our established TargetHunter, HTDocking, and BBB Predictor for target identification and polypharmacology analysis for AD research. The platform has assembled various AD-related chemogenomics data records, including 928 genes and 320 proteins related to AD, 194 AD drugs approved or in clinical trials, and 405 188 chemicals associated with 1 023 137 records of reported bioactivities from 38 284 corresponding bioassays and 10 050 references. Furthermore, we have demonstrated the application of the AlzPlatform in three case studies for identification of multitargets and polypharmacology analysis of FDA-approved drugs and also for screening and prediction of new AD active small chemical molecules and potential novel AD drug targets by our established TargetHunter and/or HTDocking programs. The predictions were confirmed by reported bioactivity data and our in vitro experimental validation. Overall, AlzPlatform will enrich our knowledge for AD target identification, drug discovery, and polypharmacology analyses and, also, facilitate the chemogenomics data sharing and information exchange/communications in aid of new anti-AD drug discovery and development.

Chemo- and bioinformatics resources for in silico drug discovery from medicinal plants beyond their traditional use: a critical review

An overview of databases andin silicotools for discovery of the hidden therapeutic potential of medicinal plants.