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The effectiveness of computational approaches as versatile tools for facilitating drug discovery and development has been recognized for decades, without exception, in the case of natural products. In the post-genomic era, scientists are bombarded with data produced by advanced technolo- gies. Thus, rendering these data into knowledge that is interpretable and meaningful becomes an essential issue. In this re- gard, computational approaches utilize the existing data to generate knowledge that provides valuable understanding for addressing current problems and guiding the further research and development of new natural-derived drugs.

Furthermore, several medicinal plants have been continuously used in many traditional medicine systems since antiquity throughout the world, and their mechanisms have not yet been elucidated.

Keywords: Natural products, Biological activity, Data mining, Drug discovery, Computer-aided drug design. Historical evidence of the first natural Historical records identified medicinal plants, fungi and products was revealed through paleoanthropological studies algae as rich sources of bioactive natural products [5]. The in which pollen deposits were found in the grave of Shanidar use of medicinal plants originated with respect to the human in present-day Iraq, which is estimated to date back to more instinct for survival, i.

The importance of natural prod- avoid death [6]. Native Americans, used ashes of the plant ucts to civilizations can be attributed to their diverse phar- genus Salvia to aid childbirth and protect infants from res- macological properties. Medical records on the use of natural piratory diseases [7]. The ancient Europeans used Parmelia products as therapeutics have been documented across re- omphalodes extracts to cure burns and cuts due to its anti- gions. Furthermore, a clay tablet depicting information re- inflammatory properties [8].

Fungi have been used as food garding medicinal extracts i. The Ebers Papyrus, an ly Chinese and Egyptian civilizations [9]. Fungi in the An- Egyptian medical text contained information on plant-based thozoans species, i. The first known Chinese for the treatment of chest infections [10]. Parmelia ompha- text on this subject was called Wu Shi Er Bing Fang con- lodes Linnaeus Acharius were widely used in the British taining 52 prescriptions , followed by Shennong Herbal Isles as a dye and in Ireland as an anti-inflammatory agent to containing drugs and Tang Herbal containing cure burns and cuts [11].

Among algae, the juice of the red drugs [4]. Furthermore, it should not be overlooked The importance of natural products in medicine has been indicated by the continual use of classical natural products. From the E-mail: chanin. Ancient physicians used it as an are in agreement with modern individualized medicine [27]. Likewise, Furthermore, recent studies have revealed the relationships they were used as painkillers during the American Civil War.

The knowledge of the Chinese and Indians has been documented. Most of the African and Asian popu- the dynamic equilibrium of these dosha is essential for nor- lations rely on traditional medicine for their primary mal bodily function [17]. In contrast, the disturbance of these healthcare [38] because of limited access to healthcare facili- dosha is believed to be the root causes of diseases [18]. In addition, the ancient use of natural yang and qi as the three main biological forces in the human products has formed the basis of later clinical, pharmacolog- body.

The balanced equilibrium of yin and yang is essential ical and chemical studies [5], which can be identified from for being healthy, and qi is required as the energy that circu- the discovery and development of many currently used lates and nourishes the entire body [19, 20]. Traditional Chi- drugs, e. The technology, regulations within the country, culture and so- Chinese and Ayurvedic traditional medicine systems have ciety [42]. The history of utilizing natural products sitions in herbal medicinal products, dietary supplements in for medicinal purposes has been noted since the Ayutthaya the USA and food supplements in the EU.

In Asian coun- period — A. The prescribed formulae can be adjusted according to that requires expertise from multidisciplinary fields. A similar basis of different body sists of many time consuming processes, from target identi- constitutions that lead to differential responses to herbs is fication to clinical trials, that require substantial financial also implied SCM [26].

According to the complexity of drug development processes, bioinformatics and computational What biomolecule can be a target? Target Data mining tools identification Molecular docking Which compounds can bind target? Conceptual framework of drug discovery and development and the roles of computational approaches. The con- from natural products, e. Target identification is the process in which drug targets are identified [43] by databases that include experimental results.

Hits are defined as groups of compounds that behaving like drugs in clinical situations. Drug likeness is exhibit desired activity in the screening process [43]. How- effective means of searching for potential compounds by ever, these rules are used as guidelines rather than as abso- using computational approaches.

One widely used computa- lute cut-offs for determining drug-like properties [44]. Re- tional method in this process is molecular docking. The crys- cently, the importance of other physicochemical and struc- tal structure of the target protein is required to simulate bind- tural properties influencing drug-like properties has been ing in silico against large libraries of compounds.

Active suggested in terms of property-based design [57]. The basis compounds with good binding affinity to the target, repre- of property-based design is that molecules with similar sented by a docking score [43], are identified as hits and will chemical structures are expected to possess similar pharma- be further developed [47].

Hits are subsequently optimized cokinetic properties [57]. The optimization is performed by The pharmacokinetic profiles of drugs, i.

Quantitative structure-activity relationships active compounds could be used as safe and efficient oral QSAR and quantitative structure-properties relationships drugs [43], and they are considered to be crucial factors for QSPR are computational methods for correlating the chem- decision-making in further development of the investigated ical structures of the compounds with their activity compounds [58].

Understanding these relationships is useful for the drug likeness of compounds and notably affect efficacy, structural modification by medicinal chemists in seeking for toxicity and drug-drug interactions [44]. For decades, many potential compounds [43, 48]. In addition, molecular model- drugs have failed and been withdrawn in the late stages of ing and molecular docking can be used for the discovery of drug development, causing considerable financial lost [43, new binding sites on target proteins [43].

The two main reasons that lead to the clinical failures of drugs are poor ADME properties [44] and severe toxicities T [43, 59]. In this regard, many ical activities [49]. In addition, the importance of most computational approaches have been employed for the pre- common molecular fragments or privileged structures has diction of ADMET properties [].

It also has been suggested that privileged structures wide range of databases to aid drug discovery efforts, and provide affinity towards binding with receptors, whereas the these can be broadly classified into two groups: bioactivity rest of the molecule defines the selectivity of a potential databases and target databases.

Privileged structures have been successfully used as core structures for the synthesis of novel biologically Bioactivity databases are valuable tools for identifying active compounds [] and as being a starting point for hit chemical compounds. For example, the ChemNavigator the synthesis of libraries [55]. Recently, activity relationship QSAR studies [68]. Pubchem is a freely accessible repository that contains more than 63 million compounds and provides Chemical Space of Natural Products diverse bioactivity results for approximately 45 million.

One of the features that makes Pubchem an attractive tool for in Chemical space is the total possible number of de- silico drug design is the PubChem Download Service [71]. Similar to the spatial Binding DB is a public and openly accessible database that extent of space the universe, these descriptors are infinite in has approximately 20, binding affinities of small com- number. Despite the advancement in the synthesis of organic pounds that have been experimentally tested with known 3D compounds and the characterization of nature products, only structural available protein targets.

Thus, by exploring the origin of chemical space in liv- Target databases are important for identifying druggable ing organisms, new strategies to combat diseases will proteins that are involved in pathogenesis. For instance, the emerge.

The Therapeutic Target Database drug-like chemical compounds. For example, Vieth et al. The results showed that the halo- binding properties and functional properties [74].

The Pro- gen contents of marketed drugs are identical, and the molec- 1. Random selection of compounds from each of the 12 databases was carried out followed by representing each compound by the ECFP substructure fingerprint. Finally, PCA was computed in R using the prcomp function from the stats package and the resulting plot is visualized using the ggplot2 package.

For example, for the structural These results were consistent with Lipinski's rule of five, properties of bioactive natural products, the molecular which claims that drugs should possess a MW smaller than weights, the number of rings, the number of carbon atoms to have good bio-absorption and bioavailability. Chemi- and the number of oxygen atoms, in particular, were higher cal space analyses were performed on natural products as than those of non-bioactive natural products [83].

In contrast, well. For instance, Ganesan [77] used 24 unique natural the results showed that most of the bioactive natural products products to understand the associated chemical space. This result suggested five, whereas the other half disobey the rule.

A closer exam- that natural products have desirable properties in drug dis- ination of the physicochemical properties of these 24 natural covery and development because compounds that obey the products revealed that almost all of them obey the log P rule, rule-of-five are orally active and very specific in binding to such that their values are smaller than 5.

Koch et al. Reayi and Arya [79] stressed pounds, of which 1, were bioactive and 1, were non- that the chemical space of natural products can be populated bioactive.

Molecular descriptors were extracted for each by diversity-oriented synthesis DOS , a strategy in chemical compound to develop a drug-likeness model using SVM as synthesis to quickly create a library of compounds, which the learning technique. The performance of the drug-likeness will aid in the deorphanization of druggable protein targets. A closer look at the key de- Osada and Hertweck [81] claimed that the chemical space of scriptors of these two models revealed by the RuleSet algo- natural products is populated naturally by gene clustering, rithm, an algorithm that is based on a decision tree algo- where gene natural product synthesizer enzymes are altered rithm, indicated only a few important descriptors to perform to increase their chemical space.

Lachance et al. In the development of the BNC-likeness claimed that the bioactivity of the chemical space of natural predictive model, descriptors were used, whereas products can be analyzed, charted and navigated to identify descriptors were used as inputs to construct the drug-likeness relevant substituents to aid modern chemical synthesis in model.

There were significant differences when the distribu- drug discovery and development. The important descriptors tural properties were compared between bioactive and non- for both models i. A dataset of 1, natural products was obtained from a total of 7, natural product ingredi- 33, 28, and 36 were mainly used to build the BNC-likeness prediction model, and they were rarely used to create drug- ents from the Ethnobotanical Database and Dr.

In contrast, clusters 19, 7, and 18 were tochemical Database. Of 1, natural products, were largely used to build drug-likeness models and were rarely bioactive whereas were not, providing a balanced da- used to make BNC-likeness models [83]. SVM with radial basis function kernels was used to perform bioactive natural compound-likeness models, using 1, compounds with bioactivity as the training set. The Natural Products as Sources of Inspiration for New performance of the models was tested with an independent Drugs external data set that included 81 bioactive natural products Small molecules and secondary metabolites have been and 81 non-bioactive natural products from widely used me- economically designed and synthesized by nature for the dicinal herbs.

The prediction results demonstrated that 75 benefit of evolution; in other words, they have been evolu- bioactive compounds were successfully classified, suggest- tionarily selected [84]. Regarding the power of evolution, ing that the models are robust and do not have the problem natural products contain diverse types of biologically rele- of overfitting. Overfitting is one of the problems in machine vant privileged structures that have saved millions of lives, learning and occurs when noise data are incorporated as in- which renders them a continuous source of inspiration for dependent variables to develop highly predictive models.

These naturally occurring Although these models work very well with internal datasets, ligands serve as excellent structural starting points for ex- their performance is very low when a new class of data or a ploring biologically relevant chemical space [86].

Therefore, test set is applied [83]. From to , natural crease structural diversity, in other words, to expand the products and their derivatives accounted for Exam- candidate drugs approved by the FDA [87]. Good examples ples of organic synthesis methods are given below. This method has been historically used naturally occurring bacterial proteasome inhibitor, epox- to yield a number of therapeutic compounds or compounds omicin.

Carfilzomib was first synthesized in by Hanada with significant impacts on mankind. A notable example of et al. However, the mechanism of action of this com- this approach is heroin, which is derived from the acetylation pound was unknown. In the late s, carfilzomib was of morphine [99]. Several research groups put cally derived fragments [97]. Statin and its derivatives, i. These com- teolix and Onyx and was approved by FDA in for the pounds have been developed from naturally occurring statin treatment of multiple myeloma [91].

In , homo- Diversity-oriented synthesis DOS is an effective tool to harringtonine was clinically observed for its anticancer po- achieve a library of structurally diverse compounds with tential against acute leukemia [92]. Since then, this natural desirable biological properties [, ].

Structural diversi- compound has been examined across several organizations ty is one of the key strategies for expanding the investigated and companies. Finally, the ester derivative of homoharring- chemical space and thereby increasing the rate of finding tonine was approved by the FDA in for the treatment of potential hits [98]. Conceptually, natural products are used as chronic myeloid leukemia under the name omacetaxine starting scaffolds to generate compound libraries by various mepesuccinate [91].

Mitoxantrone is a doxorubi- 5 steps []. Examples of natural products used as starting cin analog that was designed to minimize cardiotoxicity of scaffolds are gibberellic acid a plant hormone , adrenos- its parent compound [93].

Mitoxantrone has been approved terone steroid hormone and quinine isolated compound by the FDA for the treatment of many cancers, including from the bark of the cinchona tree []. In addi- Function-oriented synthesis FOS is an effective strategy tion, it was approved by the European Medicine Agency for producing therapeutic lead compounds in a step- EMEA of the European Union EU in September for economical fashion [95] such that small molecules are gen- the treatment of B cell lymphomas [91].

At present, there are erated with less structural complexity and with preferable applications of this drug before the FDA for approval for the properties [95]. It that are useful as structural starting points for the screening, should be noted that natural products are most likely bind to design and development of novel potential drugs.

These characteristics lead to undesired Synthesis of Natural Products side effects and inferior pharmacokinetic properties [95]. The benefits of FOS have been noted to address these prob- Natural products are in high demand owing to their ex- lems by reducing undesired side effects, enhancing desired ceptional range of bioactivities.

Some natural products are biological activities and improving pharmacokinetic proper- limited or inaccessible in nature. Organic synthesis often ties [95]. FOS has been applied for the development of many solves this problem by supplying these scarce compounds natural compounds, such as bryostatin [], halichondrin B and enabling the conversion of bioactive natural compounds [], statin [], dynemicin [] and laulimalide [95].

It is well known that the chemical structures of the majority of natural products are One of the challenges in drug discovery and development complex, which renders their total synthesis a difficult task is the identification of biologically relevant areas that are [95].

Therefore, novel organic synthetic approaches have located inside an investigated chemical space []. Biolo- been developed in an attempt to yield potential compounds gy-oriented synthesis BIOS is based on the structural anal- with medicinal value [96]. Principally, structural modifica- ysis of small molecules and target proteins, where biological tions of the natural product core structures are performed to relevance is a prime criterion for the selection of starting improve selectivity and potency, to provide additional prop- scaffolds for the synthesis of biologically active compound erties [97], and to facilitate their synthesis [95].

Furthermore, collections [84]. Therefore, biological pre-validation [84] to provide a starting point for multiple rounds of activity assays should be performed to the subsequent synthesis of small molecules enriched with obtain accurate and precise bioactivities.

Recently, QSAR biological activity [86]. In this regard, computational ap- models were successfully constructed using several bioactiv- proaches, i. It should be noted that BIOS only provides a tion, absorption, bioconcentration, permeability, metabolism, starting point for discovery, and the continuous development clearance and binding affinity [].

Initially, the chemical of practical synthetic methods, i. Many types of descriptors e. There are openly available descriptor calculators that permit descriptor extractions for the user. Hansch et al. In addition, molecular structures that represented as ], and it has also performed well for the prediction of simplified molecular-input line-entry system SMILES for- physicochemical and biological properties []. First, chemical structures are drawn, geometrically structures of natural products [].

Currently, there are optimized and calculated to obtain descriptor values. Typi- three major methods of feature selection: filter, wrapper, and cally, many types of descriptors, i. Filter approaches assess erties, molecular properties and molecular fingerprints, can the relevance of descriptors by ranking a feature relevance be extracted from the chemical structure of natural products score and filtering a feature relevance score, such as the t- using computer software [].

Although several thousand test. Subsequently, the top-ranked informative features are descriptors can be obtained from conventional software used to construct a predictive model. This approach consid- packages, those descriptors may not be informative or useful ers the intrinsic properties of the data and ignores the interac- for predicting the bioactivity of compounds of interest. Thus, tion with the model. Filter techniques are simple and fast, feature selection via machine learning algorithms is essential with little computational complexity, and they are also easy to select a set of informative descriptors prior to the con- to manipulate for very high dimensional data sets.

Bioactivities are these techniques are independent of the prediction model. This algorithm is conceptually based on a netic algorithm []. Advantages of the wrapper approaches include their interaction between the candidate feature subset distance function, such as the Euclidean distance, to measure and the model selection, whereas a common drawback of the similarity between a pair of data. In lecular descriptors, a positive integer k, and a new datum x the last category of feature selection techniques, termed em- to be classified, the k-NN algorithm finds the k nearest bedded approaches, the selection of the informative subset features is built into the model.

Similar to wrapper approach- neighbors of x in D, denoted as k-NN x , and returns the es, embedded approaches have the advantage that they can dominating class label in k-NN x as the label of x. Given include the interaction with the classification model; howev- descriptors of two compounds e. Some examples of embedded approaches included an distance Dist xi , x j is decision tree [], logistic model tree [] and random forest approaches [].

Currently, there are a behavior between a set of molecular descriptors X and a number of splitting algorithms, such as Kennard and Stone, quantitative value Y by fitting a linear equation to observed Dublex and k-means sampling.

These three algorithms were data. In MLR analysis, stepwise regression is used to select implemented with the R program within the prospectr soft- the most informative descriptor and improve the perfor- ware package.

Currently, a few well- where yi is the output value. Rather than analyzing the orig- modeling. Machine learning tasks are typically classified inal dimension of data X, the importance of the extracted into two broad categories consisting of classification and variable is more useful. In this regard, PCA is likely the most regression tasks. Classification tasks aim to discriminating a popular unsupervised learning technique based on a statisti- variable Y into its class or property, where the Y variable cal approach that reduces the dimensionality of the data set could be classified into two and more than two classes, to a smaller subset known as principal components PCs which are called binary and multi-class classification, re- while preserving its dominant characteristics variance spectively.

In contrast, the regression task primarily focuses []. The major goals of PCA are as follows: 1 to extract on predicting the value of the variable Y with a numerical the most information from X variable; 2 to analyze the pat- output.

Addi- PCA approach. The learning method is principal component analysis PCA. PLS method was proposed to solve a large number of varia- Fig. Table 1. Summary of feature selection approaches. Model Search Advantage Disadvantage Examples Filter -Independent of the classifier -Ignores interaction with the classifier -t-test -Better computational complexity than wrapper methods Wrapper -Interacts with the classifier -Risk of overfitting -Genetic algorithma -Model feature dependencies -Classifier dependent selection -Sequential forward selectionb -Sequential backward selection b Embedded -Better computational complexity than wrapper -Classifier dependent selection -Decision tree c methods -Logistic model tree d -Interacts with the classifier -Random forests e -Model feature dependencies a Reference [], b Reference [], c Reference [], d Reference [], e Reference [].

Schematic overview of commonly used machine learning techniques comprising of k-nearest neighbor A , Principal component analysis B , artificial neural network C , support vector machine D , decision tree E , and random forests F. The correlation approxi- scores of X and Y, respectively. Additional details of the PLS mation is achieved by simultaneously projecting the X and Y model can be found in references [].

ANN was proposed tes and Vapnik []. This method attempts to construct a for use with nonlinearly separable data sets. Computational separating hyperplane that maximizes the margin between models of this method were inspired by the human central the two classes of data sets.

Intuitively, a good separation or nervous system. The details of ANN evolved from the per- classification occurs when the hyperplane has the greatest ceptron concept, which is one of algorithms used for super- distance to neighboring data points of both classes because a vised classification []. Mathematically, ANN is repre- larger margin leads to lower values of the loss function of sented by a nonlinear weighted sum: the classifier and also accurately predicts each data point.

To N easily understand SVM, a linear model i. In tion. This method has been applied in both classification and SVM regression, the basic application of nonlinearly separa- regression tasks. Additional by using mapping functions. The mapping function details of the ANN method can be found in references [] and []. In particular, there are two measurements to i. The ker- portance because the MDG is suggested to be more robust nel function K x i , x j can be expressed as a similarity than the mean decrease of accuracy [].

DT, also called tree induction, models and their ability to accurately predict biological ac- was proposed to mitigate this problem by using a set of esti- tivities or chemical properties. In the classification task, four mated rules. The decision tree has an efficient built-in fea- measurements were generally used to evaluate the prediction ture importance estimator. The C4.

Often, the criteria of goodness-of-fit R2 assessed on the whole internal set and goodness-of- tree can perform well if enough internal sets are available.

The structure of DT with three nodes used to classify a com- prediction R 2pred assessed by various validation procedures pound into either active or inactive class is shown in Fig. The logistic model tree was proposed to alleviate this R 2pred and root mean square error RMSE []. The R2 and problem and can be applied to classification and regression problems [, ]. Prac- ties, respectively, while n is the number of compounds. The domain of applicability can be charac- afford the most reliable model.

Thus, the whole dataset must terized using the Euclidean distance Eq. In cases when compounds have larger leverage, 0. This test is performed by construct- ing a model in which the Y variable i.

Pharmacophores can be grouped into two classes on basic form of cross-validation is known as the k-fold cross- the basis of the method that is used to obtain them []. The validation k-fold CV. For example, for a fold CV exper- first class is structure-based pharmacophores, which is based iment, the data are first partitioned into 10 equally or nearly on probing the possible interaction points between the ligand equally sized segments or folds, then 9 segments are used and the target [].

The second class is ligand-based phar- for training and the remaining segment is used for validation. Leave-one-out cross-validation LOO-CV is a spe- dimensional structure of the target proteins for which many cial case of k-fold CV where k equals the number of instanc- active molecules are superimposed to extract the common es in the data. Thus, proach when the available data are rare, especially in bioin- pharmacophore modeling intuitively produces results per- formatics where only a few data points are available [].

Conceptually, validation, Y-randomization test, domain of applicability and common structural features of bioactive and bioinactive the William plot [].

Table 3. List of softwares related to pharmacophore modeling. Typically, parameters such as steric energies, phobic interactions [] and the three dimensional ar- electrostatic interactions and the location of an atom at lattice rangement of the target protein. The pharmacophore models intersections, together with bioactivities, are used to build were built and validated with different compound series to these predictive models [].

Because CoMFA will usually determine whether the active compounds fit the pharmaco- extract a large number of parameters, the partial least square, phore. Finally, a reliable and robust pharmacophore model a commonly used liner modeling method, simultaneously was obtained by returning a large series of compounds with projects the extracted parameters from CoMFA with bioac- preferable binding modes to the target [].

Thus, the tivities into latent variables to correlate multiple parameters pharmacophore modeling merges information from struc- with bioactivities. The capability of binding to the target protein of such chemi- DISCO DIStanceCOmparisons is the first automated cal compounds indicates that some portions of the com- pharmacophore modeling method that can systemically ana- pounds are important and are responsible for favorable inter- lyze and match the conformation of diverse molecules [] actions with the target [].

By using the insights derived by using the Bron-Kerbosh clique-detection algorithm []. However, the target and that contribute to bioactivity can be identified. DISCO has been suc- known as the 3D-QSAR method, can be used to identify cessfully utilized to identify pharmacophores of dopaminer- pharmacophores by correlating ligand 3D structures with gic agonists [], ligands of nucleoside transporters hCNT1 their binding activities.

The structures of ligands are super- [], antihypertensive drugs [], cAMP PDE III inhibi- imposed to identify common features that are responsible for tors [], neuronal nicotinic receptor agonists [], inhibi- their biological activities without requiring 3D structures of tors of vitamin D hydroxylases [] and cGMP phos- phodiesterase inhibitors [].

In any docking scheme, two conflicting re- software programs have been developed for application in quirements should be balanced: the desire for an accurate drug discovery and development [], such as GASP [], procedure and the desire to keep the computational demands HipHop [], HypoGen [], MOE [], PHASE [] at a reasonable level.

LigandScout is an integrated plat- explore all available degrees of freedom for a particular sys- form for 3D virtual screening and pharmacophore modeling. In contrast to molecular dock- trostatic and hydrophobic interactions []. These simulations generate a There has been an explosive growth in the available set of conformations of a biomolecule by iteratively integrat- structural data for proteins by X-ray crystallographic and ing numerically the equations of motion for a specific po- NMR spectroscopic studies and derived from large amounts tential function with certain initial and boundary conditions of genomic and proteomic data by theoretical modeling.

For []. A structural ensemble generated from an MD simula- this reason, discovering new drug targets relies on accurate tion can be used to explore the conformational space of bio- modeling of these data in rational drug design because in- molecule, to calculate thermodynamic quantities and to esti- formation from both protein structures and their ligand- mate the free energy of biological processes [].

In the binding sites can be exploited. In this case, two widely used prediction of the strength of non-bonded interactions, the methods, molecular docking and molecular dynamics simu- MD technique has been widely used in free energy binding lation, play a major role in these approaches and are usually calculations, which cover a broad range of accuracies and combined to investigate interactions of small molecules with computational requirements.

Other computationally expen- the protein target at the atomic level []. Several cently []. As mentioned previously, molecu- Fig. Schematic representation of the protocol combining molecular docking and molecular dynamics simulations that is applied during rational drug design such that the structure of the protein target can be experimentally or theoretically obtained. Notably, library and identify small molecules that are more likely to all of these kinases control multiple cell signaling pathways bind to the protein target.

This initial screening employs of in oncogenesis []. Recently, several compounds from the inexpensive and fast docking algorithms to evaluate the traditional Chinese medicine TCM database [] were binding affinities. Subsequently, the selected compounds successfully identified as potent inhibitors of human epider- will be subjected to additional docking experiments using mal growth factor receptor HER 1, and 2 tyrosine kinases more accurate scoring functions. Once a few hit compounds that have been known to be associated with several types of are identified, MD can then be used to refine such docked cancer have been identified by combining docking, 3D- complexes, which can account for effects of induced fit and QSAR and MD simulations [].

Finally, accurate binding free energies are then calculat- plexes. In addition to the free energy calculations, MD can ed, which makes for a rational approach that helps to im- provide valuable information by giving dynamical information prove the drug discovery process [, ]. Conse- peptides []. Several studies based on biophysical and quently, the examples given here demonstrate how one can docking experiments clearly demonstrate that various flavo- utilize MD as a tool to understand the mechanism by which noids including myricetin, quercetin, caffeic acid, daidzein, potent natural compounds act on the structure of protein tar- delphinidin, and procyanidin can bind directly to several gets.

For example, Fig. Binding pose of natural compound curcumin I, Diferuloylmethane at the active site of HER2 kinase previously investigated by Yim-Im et al. The compound and amino acid residues are represented in sticks with larger and smaller sizes, respectively carbon, grey; nitrogen, blue; oxygen, red.

Hydrogen bond and hydrophobic interactions are indicated in green and pink dashed lines, respectively. Because most natural products are not designed The prestige of traditional medicine has been recognized for human use, the transformation of naturally occurring by its effectiveness in curing diseases and its ability to im- bioactive compounds into human drugs requires modifica- prove the quality of life from antiquity [, ].

In the tion and evaluation by multidisciplinary teams of experts. Furthermore, computational chemogenomics related []. People pay more attention to traditional medicine and concepts include proteochemometric modeling, polypharma- natural products because of their concern about the adverse cology, systems pharmacology facilitates the seamless inte- side effects of synthetic drugs [].

Furthermore, most gration of bioinformatics and cheminformatics by allowing of the population in the globalization era suffers from life- the interaction of several proteins and several ligands to be style-related, stress-related and aging diseases. These chronic investigated. Such approach has great potential for drug re- diseases are related to the changing lifestyle in which society positioning, target identification, ligand profiling and recep- is more concerned about the way individuals eat and live tor deorphanization.

The return of herbal medicines can be observed from the parallel use of complementary and alternative medicines To place this field of research into greater perspective, with modern medicine to improve treatment outcomes [], populations worldwide face chronic and multifactorial dis- as well as via the trends of using natural products for the eases relating to changing lifestyles and aging conditions.

Most chronic diseases arise from unhealthy lifestyles and continual exposure to harmful Many countries have established unique herbal medicine chemicals. This situation ought to stimulate people to have systems with regards to their cultural history, ecology and greater concern about how they spend their life. Healthy life- medical anthropology [15]. Most of the traditional medicine styles and eco-friendly products are becoming fashionable systems often prescribe combinations of herbal mixtures, and for new generations.

In addition, the polypharmacology- their therapeutic effects are based on synergistic or antago- based principle of traditional medicine is expected to provide nistic effects among each other [15]. Although it is widely favorable treatment outcomes against multifactorial diseases. World Health Organization WHO has encouraged a prime Furthermore, significant attention has been given to natural focus on herbal medicines to standardize regulations across products because of their influence on human well-being, as countries and promote their safety and efficacy [, ].

In summary, natural discovery of new drugs and are of great value to the field of products are of great benefit to mankind, and extensive re- drug development []. With respect to the power of Mother search on these natural treasures would provide substantial Nature, all organisms select chemicals for synthesis, consump- impact for the betterment of society. It should be noted that some of these scaffolds share common core The author s confirm that this article content has no con- structures with different substituent patterns, which give rise flict of interest.

Hence, This research project is supported by the annual budget scaffolds from natural products serve as structural starting grant of Mahidol University B. University under the National Research Universities Initia- Computational approaches are fundamental for drug dis- tive. Shanidar IV, a Neanderthal flower burial in northern hits and druggable targets.

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Pharmacogenomics of a tradi- [7] Hicks, S. Desert plants and people, 2nd ed. Can- Book Publisher of the Southwest: Texas, Metabolic diversity of lichen-forming asco- [33] Matsumoto, C. A historical overview of natu- Shimada, Y. A ral products in drug discovery. Metabolites, , 2 2 , Present and future needs for algae and algal products. Based Complement. Kim, Y. A genome-wide scan [11] Smith, E.

Medicinal plants in folk tradition: An ethnobotany of for the Sasang constitution in a Korean family suggests significant Britain and Ireland. HortScience, , 40 3 , The natural history of Cornwall; E. Books, Alkaloids from callus tissue of S. Association between genetic polymor- Papaver somniferum. Phytochemistry, , 11 10 , In: Van Nostrand's Evid. Sasang constitution as a risk factor for [15] Leonti, M. Traditional medicines and globalization: current and diabetes mellitus: a cross-sectional study.

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Economic policy Pages 2. Wimbledon 84 Pages 2. Thekla Pages 1.