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Docking (molecular)From Wikipedia, the free encyclopedia

Docking glossary

Receptor orhost The "receiving" molecule, most commonlya protein or other biopolymer.

Ligand orguest The complementary partner moleculewhich binds to the receptor. Ligands are most often smallmolecules but could also be another biopolymer.

Docking Computational simulation of a candidate ligand binding

to a receptor.

Binding mode The orientation of the ligand relative to thereceptor as well as the conformation of the ligand and receptor whenbound to each other.

Pose A candidate binding mode.

Scoring The process of evaluating a particular pose by countingthe number of favorable intermolecular interactionssuch as hydrogenbonds and hydrophobic contacts.

Ranking The process of classifying which ligands are most likelyto interact favorably to a particular receptor based on thepredicted free-energy of binding.

Schematic diagram illustrating the docking of a small moleculeligand (brown) to a protein receptor (green) to produce a complex.


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Small molecule docked to a protein.

In the field of molecular modeling, docking is a method which predicts the preferred orientation of one

molecule to a second when bound to each other to form a stable complex. [1] Knowledge of the preferred

orientation in turn may be used to predict the strength of association or binding affinitybetween two molecules

using for example scoring functions.

The associations between biologically relevant molecules such as proteins,nucleic acids, carbohydrates,

and lipids play a central role in signal transduction. Furthermore, the relative orientation of the two interacting

partners may affect the type of signal produced (e.g., agonism vsantagonism). Therefore docking is useful for

predicting both the strength and type of signal produced.

Docking is frequently used to predict the binding orientation of small molecule drug candidates to their protein

targets in order to in turn predict the affinity and activity of the small molecule. Hence docking plays an

important role in the rational design of drugs. [2] Given the biological andpharmaceutical significance of

molecular docking, considerable efforts have been directed towards improving the methods used to predict

docking .

Contents

[hide]

1 Definition of problem

2 Docking approaches

o 2.1 Shape complementarity

o 2.2 Simulation

3 Mechanics of docking

o 3.1 Search algorithm

3.1.1 Ligand flexibility

3.1.2 Receptor flexibility

o 3.2 Scoring function

4 Applications

5 See also

6 References

7 External links

[ ]Definition of problem


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Molecular docking can be thought of as a problem of lock-and-key, where one is interested in finding the

correct relative orientation of the keywhich will open up the lock(where on the surface of the lock is the key

hole, which direction to turn the key after it is inserted, etc.). Here, the protein can be thought of as the lock

and the ligand can be thought of as a key. Molecular docking may be defined as an optimization problem,

which would describe the best-fit orientation of a ligand that binds to a particular protein of interest. However

since both the ligand and the protein are flexible, a hand-in-gloveanalogy is more appropriate than lock-and-

key.[3] During the course of the process, the ligand and the protein adjust their conformation to achieve an

overall best-fit and this kind of conformational adjustments resulting in the overall binding is referred to

as induced-fit.[4]

The focus of molecular docking is to computationally simulate the molecular recognition process. The aim of

molecular docking is to achieve an optimized conformation for both the protein and ligand and relative

orientation between protein and ligand such that the free energy of the overall system is minimized.

[ ]Docking approaches

Two approaches are particularly popular within the molecular docking community. One approach uses a

matching technique that describes the protein and the ligand as complementary surfaces. [5][6] The second

approach simulates the actual docking process in which the ligand-protein pairwise interaction energies are

calculated.[7] Both approaches have significant advantages as well as some limitations. These are outlined

below.

[ ]Shape complementarity

Geometric matching/ shape complementarity methods describe the protein and ligand as a set of features that

make them dockable.[8]These features may include molecular surface/ complementary surface descriptors. In

this case, the receptors molecular surface is described in terms of its solvent-accessible surface area and the

ligands molecular surface is described in terms of its matching surface description. The complementarity

between the two surfaces amounts to the shape matching description that may help finding the complementary

pose of docking the target and the ligand molecules. Another approach is to describe the hydrophobic features

of the protein using turns in the main-chain atoms. Yet another approach is to use a Fourier shape descriptor

technique.[9][10][11] Whereas the shape complementarity based approaches are typically fast and robust, they

cannot usually model the movements or dynamic changes in the ligand/ protein conformations accurately,

although recent developments allow these methods to investigate ligand flexibility. Shape complementarity

methods can quickly scan through several thousand ligands in a matter of seconds and actually figure out

whether they can bind at the proteins active site, and are usually scalable to even protein-protein interactions.

They are also much more amenable to pharmacophore based approaches, since they use geometric

descriptions of the ligands to find optimal binding.


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[ ]Simulation

The simulation of the docking process as such is a much more complicated process. In this approach, the

protein and the ligand are separated by some physical distance, and the ligand finds its position into the

proteins active site after a certain number of moves in its conformational space. The moves incorporate rigid

body transformations such as translations and rotations, as well as internal changes to the ligands structure

including torsion angle rotations. Each of these moves in the conformation space of the ligand induces a total

energetic cost of the system, and hence after every move the total energy of the system is calculated. The

obvious advantage of the method is that it is more amenable to incorporate ligand flexibility into its modeling

whereas shape complementarity techniques have to use some ingenious methods to incorporate flexibility in

ligands. Another advantage is that the process is physically closer to what happens in reality, when the protein

and ligand approach each other after molecular recognition. A clear disadvantage of this technique is that it

takes longer time to evaluate the optimal pose of binding since they have to explore a rather large energy

landscape. However grid-based techniques as well as fast optimization methods have significantly ameliorated

these problems.

[ ]Mechanics of docking

To perform a docking screen, the first requirement is a structure of the protein of interest. Usually the structure

has been determined using a biophysical technique such as x-ray crystallography, or less often, NMR

spectroscopy. This protein structure and a database of potential ligands serve as inputs to a docking program.

The success of a docking program depends on two components: the search algorithm and thescoring function.

[ ]Search algorithm

Main article: Searching the Conformational Space for Docking

The search space in theory consists of all possible orientations and conformations of the protein paired with the

ligand. However in practice with current computational resources, it is impossible to exhaustively explore the

search spacethis would involve enumerating all possible distortions of each molecule (molecules are

dynamic and exist in an ensemble of conformational states) and all possible rotational and translational

orientations of the ligand relative to the protein at a given level of granularity. Most docking programs in use

account for a flexible ligand, and several attempt to model a flexible protein receptor. Each "snapshot" of the

pair is referred to as a pose.

A variety of conformational search strategies have been applied to the ligand and to the receptor. These

include:

systematic or stochastic torsional searches about rotatable bonds

molecular dynamics simulations


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genetic algorithms to "evolve" new low energy conformations

[ ]Ligand flexibility

Conformations of the ligand may be generated in the absence of the receptor and subsequently docked [12] or

conformations may be generated on-the-fly in the presence of the receptor binding cavity. [13] Force field energy

evaluation are most often used to select energetically reasonable conformations, [14] but knowledge-based

methods have also been used.[15]

[ ]Receptor flexibility

Computational capacity has increased dramatically over the last decade making possible the use of more

sophisticated and computationally intensive methods in computer-assisted drug design. However, dealing with

receptor flexibility in docking methodologies is still a thorny issue. The main reason behind this difficulty is the

large number of degrees of freedom that have to be considered in this kind of calculations. However, neglecting

it, leads to poor docking results in terms of binding pose prediction. [16]

Multiple static structures experimentally determined for the same protein in different conformations are often

used to emulate receptor flexibility.[17] Alternatively rotamer libraries of amino acid side chains that surround the

binding cavity may be searched to generate alternate but energetically reasonable protein conformations.[18][19]

[ ]Scoring function

Main article: Scoring Functions for Docking

The scoring function takes a pose as input and returns a number indicating the likelihood that the pose

represents a favorable binding interaction.

Most scoring functions are physics-based molecular mechanics force fields that estimate the energy of the

pose; a low (negative) energy indicates a stable system and thus a likely binding interaction. An alternative

approach is to derive a statistical potential for interactions from a large database of protein-ligand complexes,

such as the Protein Data Bank, and evaluate the fit of the pose according to this inferred potential.

There are a large number of structures from X-ray crystallography for complexes between proteins and high

affinity ligands, but comparatively fewer for low affinity ligands as the later complexes tend to be less stable and

therefore more difficult to crystallize. Scoring functions trained with this data can dock high affinity ligands

correctly, but they will also give plausible docked conformations for ligands that do not bind. This gives a large

number of false positive hits, i.e., ligands predicted to bind to the protein that actually don't when placed

together in a test tube.

One way to reduce the number of false positives is to recalculate the energy of the top scoring poses using

(potentially) more accurate but computationally more intensive techniques such as Generalized

Born or Poisson-Boltzmann methods.[7]


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[ ]Applications

A binding interaction between a small molecule ligand and an enzyme protein may result in activation

or inhibition of the enzyme. If the protein is a receptor, ligand binding may result in agonism or antagonism.

Docking is most commonly used in the field of drug design most drugs are small organic molecules, anddocking may be applied to:

hit identification docking combined with a scoring function can be used to

quickly screen large databases of potential drugs in silico to identify molecules

that are likely to bind to protein target of interest (see virtual screening).

lead optimization docking can be used to predict in where and in which

relative orientation a ligand binds to a protein (also referred to as the binding

mode or pose). This information may in turn be used to design more potent

and selective analogs.

Bioremediation Protein ligand docking can also be used to predict pollutants

that can be degraded by enzymes. [20]

[ ]References

1. ^ Lengauer T, Rarey M (1996). "Computational methods for biomolecular

docking". Curr. Opin. Struct. Biol.6 (3): 4026. doi:10.1016/S0959-

440X(96)80061-3. PMID 8804827.

2.^

Kitchen DB, Decornez H, Furr JR

, Bajorath J (2004). "Docking and scoringin virtual screening for drug discovery: methods and applications".Nature

reviews. Drug discovery3 (11): 935

49. doi:10.1038/nrd1549. PMID 15520816.

3. ^ Jorgensen WL (1991). "Rusting of the lock and key model for protein-ligand

binding". Science254 (5034): 954

5.doi:10.1126/science.1719636. PMID 1719636.

4. ^ Wei BQ, Weaver LH, Ferrari AM, Matthews BW, Shoichet BK (2004).

"Testing a flexible-receptor docking algorithm in a model binding site". J. Mol.

Biol.337 (5): 116182. doi:10.1016/j.jmb.2004.02.015. PMID 15046985.

5. ^Meng EC, Shoichet BK, Kuntz ID (2004). "Automated docking with grid-

based energy evaluation". Journal of Computational Chemistry13 (4): 505

524. doi:10.1002/jcc.540130412.

6. ^Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson

AJ (1998). "Automated docking using a Lamarckian genetic algorithm and an


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empirical binding free energy function". Journal of Computational

Chemistry19 (14): 16391662. doi:10.1002/(SICI)1096-

987X(19981115)19:143.0.CO;2-B.

7. ^ab

Feig M, Onufriev A, Lee MS, Im W, Case DA, Brooks CL (2004).

"Performance comparison of generalized born and Poisson methods in the

calculation of electrostatic solvation energies for protein structures". Journal

of Computational Chemistry25 (2): 265

84.doi:10.1002/jcc.10378. PMID 14648625.

8. ^ Shoichet BK, Kuntz ID, Bodian DL (2004). "Molecular docking using shape

descriptors". Journal of Computational Chemistry13 (3): 380

397.doi:10.1002/jcc.540130311.

9. ^Cai W, Shao X, Maigret B (January 2002). "Protein-ligand recognition using

spherical harmonic molecular surfaces: towards a fast and efficient filter for

large virtual throughput screening". J. Mol. Graph. Model.20 (4): 313

28. doi:10.1016/S1093-3263(01)00134-6.PMID 11858640.

10. ^Morris RJ, Najmanovich RJ, Kahraman A, Thornton JM (May 2005). "Real

spherical harmonic expansion coefficients as 3D shape descriptors for protein

binding pocket and ligand comparisons". Bioinformatics21 (10): 2347

55. doi:10.1093/bioinformatics/bti337. PMID 15728116.

11. ^ Kahraman A, Morris RJ, Laskowski RA, Thornton JM (April 2007). "Shape

variation in protein binding pockets and their ligands". J. Mol. Biol.368 (1):

283301. doi:10.1016/j.jmb.2007.01.086. PMID 17337005.

12. ^ Kearsley SK, Underwood DJ, Sheridan RP, MillerMD (October 1994).

"Flexibases: a way to enhance the use of molecular docking methods".J.

Comput. AidedMol. Des.8 (5): 565

82. doi:10.1007/BF00123666. PMID 7876901.

13. ^ FriesnerRA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT,

Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin

PS (March 2004). "Glide: a new approach for rapid, accurate docking and

scoring. 1. Method and assessment of docking accuracy". J. Med.

Chem.47 (7): 173949. doi:10.1021/jm0306430. PMID 15027865.

14. ^ Wang Q, Pang YP (2007). "Preference of small molecules for local

minimum conformations when binding to proteins". PLoS ONE2 (9):

e820. doi:10.1371/journal.pone.0000820. PMID 17786192.


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Docking software

Click2Drug.org - Directory of computational drug design tools.

Drug discoveryFrom Wikipedia, the free encyclopedia

In the fields of medicine, biotechnology and pharmacology, drug discovery is the process by which drugs are

discovered and/or designed.

In the past most drugs have been discovered either by identifying the active ingredient from traditional

remedies or by serendipitous discovery. A new approach has been to understand

how disease and infection are controlled at the molecular and physiological level and to target specific entities

based on this knowledge.

The process of drug discovery involves the identification of candidates, synthesis, characterization, screening,

and assays for therapeutic efficacy. Once a compound has shown its value in these tests, it will begin the

process of drug development prior to clinical trials.

Despite advances in technology and understanding of biological systems, drug discovery is still a lengthy,

"expensive, difficult, and inefficient process" with low rate of new therapeutic discovery.[1]Currently, the

research and development cost of each new molecular entity (NME) is approximately US$1.8 billion.[2]

Information on the human genome, its sequence and what it encodes has been hailed as a potential windfall for

drug discovery, promising to virtually eliminate the bottleneck in therapeutic targets that has been one limiting

factor on the rate of therapeutic discovery. [citation needed]However, data indicates that "new targets" as opposed to

"established targets" are more prone to drug discovery project failure in general[citation needed] This data

corroborates some thinking underlying a pharmaceutical industry trend beginning at the turn of the twenty-first

century and continuing today which finds more risk aversion in target selection among multi-national

pharmaceutical companies.[citation needed]

Contents

[hide]

1 Drug targets

2 Screening and design

3 Historical background

4 Nature as source of drugs

o 4.1 Plant-derived

o 4.2 Microbial metabolites

o 4.3 Marine invertebrates


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5 Chemical diversity of natural products

6 Natural product drug discovery

o 6.1 Screening

o

6.2

Structural elucidation

7 See also

8 References

9 Further reading

10 External links

[ ]Drug targets

The definition of "target" itself is something argued within the pharmaceutical industry. Generally, the "target" is

the naturally existing cellular or molecular structure involved in the pathology of interest that the drug-in-

development is meant to act on. However, the distinction between a "new" and "established" target can be

made without a full understanding of just what a "target" is. This distinction is typically made by pharmaceutical

companies engaged in discovery and development of therapeutics.

"Established targets" are those for which there is a good scientific understanding, supported by a lengthy

publication history, of both how the target functions in normal physiology and how it is involved in human

pathology. This does not imply that the mechanism of action of drugs that are thought to act through a

particular established targets is fully understood. Rather, "established" relates directly to the amount of

background information available on a target, in particular functional information. The more such information is

available, the less investment is (generally) required to develop a therapeutic directed against the target. The

process of gathering such functional information is called "target validation" in pharmaceutical industry

parlance. Established targets also include those that the pharmaceutical industry has had experience mounting

drug discovery campaigns against in the past; such a history provides information on the chemical feasibility of

developing a small molecular therapeutic against the target and can provide licensing opportunities and

freedom-to-operate indicators with respect to small-molecule therapeutic candidates.

In general, "new targets" are all those targets that are not "established targets" but which have been or are the

subject of drug discovery campaigns. These typically include newly discovered proteins, or proteins whose

function has now become clear as a result of basic scientific research.

The majority of targets currently selected for drug discovery efforts are proteins. Two classes predominate: G-

protein-coupled receptors (or GPCRs) and protein kinases.

[ ]Screening and design


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The process of finding a new drug against a chosen target for a particular disease usually involves high-

throughput screening (HTS), wherein large libraries of chemicals are tested for their ability to modify the target.

For example, if the target is a novel GPCR, compounds will be screened for their ability to inhibit or stimulate

that receptor (see antagonist and agonist): if the target is a protein kinase, the chemicals will be tested for their

ability to inhibit that kinase.

Another important function of HTS is to show how selective the compounds are for the chosen target. The ideal

is to find a molecule which will interfere with only the chosen target, but not other, related targets. To this end,

other screening runs will be made to see whether the "hits" against the chosen target will interfere with other

related targets - this is the process ofcross-screening. Cross-screening is important, because the more

unrelated targets a compound hits, the more likely that off-target toxicity will occur with that compound once it

reaches the clinic.

It is very unlikely that a perfect drug candidate will emerge from these early screening runs. It is more often

observed that several compounds are found to have some degree of activity, and if these compounds share

common chemical features, one or more pharmacophores can then be developed. At this point, medicinal

chemists will attempt to use structure-activity relationships (SAR) to improve certain features of thelead

compound:

increase activity against the chosen target

reduce activity against unrelated targets

improve the "drug-like" or ADME properties of the molecule.

This process will require several iterative screening runs, during which, it is hoped, the properties of the new

molecular entities will improve, and allow the favoured compounds to go forward to in vitro and in vivo testing

for activity in the disease model of choice.

While HTS is a commonly used method for novel drug discovery, it is not the only method. It is often possible to

start from a molecule which already has some of the desired properties. Such a molecule might be extracted

from a natural product or even be a drug on the market which could be improved upon (so-called "me too"

drugs). Other methods, such as virtual high throughput screening, where screening is done using computer-

generated models and attempting to "dock" virtual libraries to a target, are also often used.

Another important method for drug discovery is drug design, whereby the biological and physical properties of

the target are studied, and a prediction is made of the sorts of chemicals that might (eg.) fit into an active site.

One example is fragment-based lead discovery (FBLD). Novel pharmacophores can emerge very rapidly from

these exercises.


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Once a lead compound series has been established with sufficient target potency and selectivity and

favourable drug-like properties, one or two compounds will then be proposed for drug development. The best of

these is generally called the lead compound, while the other will be designated as the "backup".

[ ]Historical background

The idea that effect of drug in human body are mediated by specific interactions of the drug molecule with

biological macromolecules, (proteins or nucleic acids in most cases) led scientists to the conclusion that

individual chemicals are required for the biological activity of the drug. This made for the beginning of the

modern era in pharmacology, as pure chemicals, instead of crude extracts, became the standard drugs.

Examples of drug compounds isolated from crude preparations are morphine, the active agent in opium,

and digoxin, a heart stimulant originating from Digitalis lanata. Organic chemistry also led to the synthesis of

many of the cochemicals isolated from biological sources.

[ ]Nature as source of drugs

Despite the rise of combinatorial chemistry as an integral part of lead discovery process, the natural products

still play a major role as starting material for drug discovery.[3] A report was published in 2007 [4], covering years

1981-2006 details the contribution of biologically occurring chemicals in drug development. According to this

report, of the 974 small molecule new chemical entities, 63% were natural derived or semisynthetic derivatives

of natural products. For certain therapy areas, such as antimicrobials, antineoplastics, antihypertensive and

anti-inflammatory drugs, the numbers were higher.

Natural products may be useful as a source of novel chemical structures for modern techniques of

development of antibacterial therapies.[5]

Despite the implied potential, only a fraction of Earths living species has been tested for bioactivity.

[ ]Plant-derived

Prior to Paracelsus, the vast majority of traditionally used crude drugs in Western medicine were plant-derived

extracts. This has resulted in a pool of information about the potential of plant species as an important source

of starting material for drug discovery. A different set of metabolites is sometimes produced in the different

anatomical parts of the plant (e.. root, leaves and flower), and botanical knowledge is crucial also for the correct

identification of bioactive plant materials.

[ ]Microbial metabolites

Microbes compete for living space and nutrients. To survive in these conditions, many microbes have

developed abilities to prevent competing species from proliferating. Microbes are the main source of

antimicrobial drugs. Streptomyces species have been a source of antibiotics. The classical example of an


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antibiotic discovered as a defense mechanism against another microbe is the discovery of penicillin in bacterial

cultures contaminated by Penicillium fungi in 1928.

[ ]Marine invertebrates

Marine environments are potential sources for new bioactive agents.

[6]

Arabinose nucleosides discovered frommarine invertebates in 1950s, demonstrating for the first time that sugar moieties other than ribose and

deoxyribose can yield bioactive nucleoside structures. However, it was 2004 when the first marine-derived drug

was approved. The cone snail toxin ziconotide, also known as Prialt, was approved by the Food and Drug

Administration to treat severe neuropathic pain. Several other marine-derived agents are now in clinical trials

for indications such as cancer, anti-inflammatory use and pain. One class of these agents are bryostatin-like

compounds,under investigation as anti-cancer therapy.

[ ]Chemical diversity of natural products

As above mentioned, combinatorial chemistry was a key technology enabling the efficient generation of large

screening libraries for the needs of high-throughput screening. However, now, after two decades of

combinatorial chemistry, it has been pointed out that despite the increased efficiency in chemical synthesis, no

increase in lead or drug candidates has been reached [4]. This has led to analysis of chemical characteristics of

combinatorial chemistry products, compared to existing drugs and/or natural products.

The chemoinformatics concept chemical diversity, depicted as distribution of compounds in the chemical

space based on their physicochemical characteristics, is often used to describe the difference between the

combinatorial chemistry libraries and natural products. The synthetic, combinatorial library compounds seem to

cover only a limited and quite uniform chemical space, whereas existing drugs and particularly naturalproducts, exhibit much greater chemical diversity, distributing more evenly to the chemical space [3] . The most

prominent differences between natural products and compounds in combinatorial chemistry libraries is the

number of chiral centers (much higher in natural compounds), structure rigidity (higher in natural compounds)

and number of aromatic moieties (higher in combinatorial chemistry libraries). Other chemical differences

between these two groups include the nature of heteroatoms (O and N enriched in natural products, and S and

halogen atoms more often present in synthetic compounds), as well as level of non-aromatic unsaturation

(higher in natural products). As both structure rigidity and chirality are both well-established factors in medicinal

chemistry known to enhance compounds specificity and efficacy as a drug, it has been suggested that natural

products compare favourable to today's combinatorial chemistry libraries as potential lead molecules.

[ ]Natural product drug discovery

[ ]Screening

Two main approaches exist for the finding of new bioactive chemical entities from natural sources: random

collection and screening of material; and exploitation of ethnopharmacological knowledge in the selection. The


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former approach is based on the fact that only a small part of Earths biodiversity has ever been tested for

pharmaceutical activity, and organisms living in a species-rich environment need to evolve defensive and

competitive mechanisms to survive. A collection of plant, animal and microbial samples from rich ecosystems

might give rise to novel biological activities. One example of a successful use of this strategy is the screening

for antitumour agents by the National CancerInstitute, started in the 1960s. Paclitaxel was identified from

Pacific yew tree Taxus brevifolia. Paclitaxel showed anti-tumour activity by a previously undescribed

mechanism (stabilization of microtubules) and is now approved for clinical use for the treatment of lung, breast

and ovarian cancer, as well as for Kaposi's sarcoma.

Ethnobotany is the study of the use of plants in the society, and ethnopharmacology, an area inside

ethnobotany, is focused on medicinal use of plants. Both can be used in selecting starting materials for future

drugs. Artemisinin, an antimalarial agent from sweet wormtreeArtemisia annua, used in Chinese medicine

since 200BC is one drug used as part of combination therapy for multiresistant Plasmodium falciparum.

[ ]Structural elucidation

The elucidation of the chemical structure is critical to avoid the re-discovery of a chemical agent that is already

known for its structure and chemical activity. Mass spectrometry, often used to determine structure, is a method

in which individual compounds are identified based on their mass/charge ratio, after ionization. Chemical

compounds exist in nature as mixtures, so the combination of liquid chromatography and mass spectrometry

(LC-MS) is often used to separate the individual chemicals. Databases of mass spectras for known compounds

are available. Nuclear magnetic resonance spectroscopy is another important technique for determining

chemical structures of natural products. NMR yields information about individual hydrogen and carbon atoms in

the structure, allowing detailed reconstruction of the molecules architecture.

[ ]

[ ]References

1. ^ Anson, Blake D.; Ma, Junyi; He, Jia-Qiang (1 May 2009), "Identifying

CardiotoxicCompounds", GeneticEngineering & BiotechnologyNews,

TechNote (Mary Ann Liebert) 29 (9): 3435, ISSN 1935-

472X, OCLC 77706455, archived from the original on 25 July 2009, retrieved

25 July 2009

2. ^ Steven M. Paul, Daniel S. Mytelka, Christopher T. Dunwiddie, Charles C.

Persinger, Bernard H. Munos, Stacy R. Lindborg & Aaron L. Schacht (2010).

"How to improve R&D productivity: the pharmaceutical industry's grand

challenge". Nature Reviews Drug Discovery9 (3): 203

214.doi:10.1038/nrd3078. PMID 20168317.


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3. ^ ab FeherM, Schmidt JM (2003). "Property distributions: differences

between drugs, natural products, and molecules from combinatorial

chemistry". JChem Inf Comput Sci43 (1): 218

27. doi:10.1021/ci0200467. PMID 12546556.

4. ^ ab Newman DJ, Cragg GM (March 2007). "Natural products as sources of

new drugs over the last 25 years". J. Nat. Prod.70 (3): 461

77.doi:10.1021/np068054v. PMID 17309302.

5. ^ von Nussbaum F, Brands M, Hinzen B, Weigand S, Hbich D (August

2006). "Antibacterial natural products in medicinal chemistry--exodus or

revival?".Angew. Chem. Int. Ed. Engl.45 (31): 5072

129. doi:10.1002/anie.200600350. PMID 16881035. "The handling of natural

products is cumbersome, requiring nonstandardized workflows and extended

timelines. Revisiting natural products with modern chemistry and target-finding tools from biology (reversed genomics) is one option for their revival.".

6. ^ John Faulkner D, Newman DJ, Cragg GM (February 2004). "Investigations

of the marine flora and fauna of the Islands of Palau". NatProd Rep21 (1):

5076. doi:10.1039/b300664f. PMID 15039835.

[ ]Further reading

Gad, Shayne C. (2005). Drug discovery handbook. Hoboken, N.J: Wiley-

Interscience/J. Wiley. ISBN 0-471-21384-5.

Madsen, Ulf; Krogsgaard-Larsen, Povl; Liljefors, Tommy (2002). Textbook of

drug design and discovery. Washington, DC: Taylor & Francis.ISBN 0-415-

28288-8.

[ ]External links

Introduction to Drug Discovery - Combinatorial Chemistry Review

In Focus "Medical Research involving Minors: Medical, legal and ethical

aspects" (German Reference Centre for Ethics in the Life Sciences)

International Conference on Harmonisation of Technical Requirements for

Registration of Pharmaceuticals for Human Use (ICH)

Food and Drug Administration (FDA)

CDER Drug and Biologic Approval Reports

Pharmaceutical Research and Manufacturers of America (PhRMA)

European Medicines Agency (EMEA)


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Pharmaceuticals and Medical Devices Agency (PMDA)

WHO Model List of Essential Medicines

Innovation and Stagnation: Challenge and Opportunity on the Critical Path to

New Medical Products - FDA

Priority Medicines for Europe and the World Project "A Public Health

Approach to Innovation" - WHO

International Union of Basic and Clinical Pharmacology

IUPHARCommittee on Receptor Nomenclature and Drug Classification

Advanced Cell Classifier program for high-content screen analysis (ETH

Zurich)

Drugdiscovery@home Early in silico drug discovery by volunteer computing.

Supercomputing Facility for Bioinformatics &Computational Biology, IIT Delhi

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What is Drug Design ?

Drug discovery and development is an intense, lengthy and an interdisciplinary endeavor.Drug discovery is mostly portrayed as a linear, consecutive process that starts withtarget and lead discovery, followed by lead optimization and pre-clinical in vitro and in

vivo studies to determine if such compounds satisfy a number of pre-set criteria forinitiating clinical development. For the pharmaceutical industry, the number of years tobring a drug from discovery to market is approximately 12-14 years and costing upto$1.2 - $1.4 billion dollars. Traditionally, drugs were discovered by synthesizingcompounds in a time-consuming multi-step processes against a battery ofinvivo biological screens and further investigating the promising candidates for theirpharmacokinetic properties, metabolism and potential toxicity. Such a developmentprocess has resulted in high attrition rates with failures attributed to poorpharmacokinetics (39%), lack of efficacy (30%), animal toxicity (11%), adverse effectsin humans (10%) and various commercial and miscellaneous factors. Today, the processof drug discovery has been revolutionized with the advent of genomics, proteomics,bioinformatics and efficient technologies like, combinatorial chemistry, high throughputscreening (HTS), virtual screening, de novo design, in vitro, in silicoADMET screening andstructure-based drug design.

What is in-silico Drug Design ?


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In silico methods can help in identifying drug targets via bioinformatics tools.They can also be used to analyze the target structures for possible binding/ active sites,generate candidate molecules, check for their drug likeness , dock these molecules withthe target , rank them according to their binding affinites , further optimize the moleculesto improve binding characteristics

The use of computers and computational methods permeates all aspects of drugdiscovery today and forms the core of structure-based drug design. High-performancecomputing, data management software and internet are facilitating the access of hugeamount of data generated and transforming the massive complex biological data intoworkable knowledge in modern day drug discovery process. The use of complementaryexperimental and informatics techniques increases the chance of success in many stagesof the discovery process, from the identification of novel targets and elucidation of theirfunctions to the discovery and development of lead compounds with desired properties.Computational tools offer the advantage of delivering new drug candidates more quicklyand at a lower cost. Major roles of computation in drug discovery are; (1) Virtualscreening & de novo design, (2) in silicoADME/T prediction and (3) Advanced methods fordetermining protein-ligand binding

Why in-silico Drug Design is significant ?

As structures of more and more protein targets become available throughcrystallography, NMR and bioinformatics methods, there is an increasing demand forcomputational tools that can identify and analyze active sites and suggest potential drugmolecules that can bind to these sites specifically. Also to combat life-threateningdiseases such as AIDS, Tuberculosis, Malaria etc., a global push is essential. Millions for

Viagra and pennies for the diseases of the poor is the current situation of investment inPharma R&D. Time and cost required for designing a new drug are immense and at anunacceptable level. According to some estimates it costs about $880 million and 14 yearsof research to develop a new drug before it is introduced in the market Intervention ofcomputers at some plausible steps is imperative to bring down the cost and timerequired in the drug discovery process


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Introduction to Drug Discovery

Drug discovery and development is an expensive process due to thehigh costs of R&D and human clinical tests. The average total cost perdrug development varies from US$ 897 million to US$ 1.9 billion. Thetypical development time is 10-15 years.

R&D of a new drug involves the identification of a target (e.g. protein)and the discovery of some suitable drug candidates that can block oractivate the target. Clinical testing is the most extensive and expensivephase in drug development and is done in order to obtain the necessarygovernmental approvals. In the US drugs must be approved by the Food

and Drug Administration (FDA).

R&D Finding the Drug

One of the most successful ways to find promising drug candidates is toinvestigate how the target protein interacts with randomly chosencompounds, which are usually a part of compound libraries. This testingis often done in so called high-thoughput screening (HTS) facilities.Compound libraries are commercially available in sizes of up to severalmillions of compounds. The most promising compounds obtained fromthe screening are called hits these are the compounds that showbinding activity towards the target.

Some of these hits are then promoted to lead compounds candidate

structures which are further refined and modified in order to achievemore favorable interactions and less side-effects.

Drug Discovery Methods

The following are methods for finding a drug candidate, along with theirpros and cons:1. Virtual screening (VS) based on the computationally inferred orsimulated real screening;The main advantages of this method compared to laboratoryexperiments are:-low costs, no compounds have to be purchased externally orsynthesized by a chemist;-it is possible to investigate compounds that have not been synthesized

yet;-conducting HTS experiments is expensive and VS can be used toreduce the initial number of compounds before using HTS methods;-huge amount of chemicals to search from. The number of possiblevirtual molecules available for VS is exceedingly higher than the numbeof compounds presently available for HTS;The disadvantage of virtual screening is that it can not substitute thereal screening.2. The real screening, such as high-throughput screening (HTS), can


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experimentally test the activity of hundreds of thousands of compoundsagainst the target a day. This method provides real results that areused for drug discovery. However, it is highly expensive.

Virtual Screening in Drug Discovery

Computational methods can be used to predict or simulate how a

particular compound interacts with a given protein target. They can beused to assist in building hypotheses about desirable chemicalproperties when designing the drug and, moreover, they can be used torefine and modify drug candidates. The following three virtual screeningor computational methods are used in the modern drugdiscovery process: Molecular Docking, Quantitative Structure-ActivityRelationships (QSAR) and Pharmacopoeia Mapping.

Molecular Docking

When the structure of the target is available, usually from X-raycrystallography, the most commonly used virtual screening method ismolecular docking. Molecular docking can also be used to test possiblehypotheses before conducting costly laboratory experiments. Molecular

docking programs predict how a drug candidate binds to a proteintarget. This software consists of two core components:

1. A search algorithm, sometimes called an optimisation algorithm. Thesearch algorithm is responsible for finding the best conformations of theligand, a small drug-like molecule and protein system. A conformation ithe position and orientation of the ligand relative to the protein. Inflexible docking the conformation also contains information about theinternal flexible structure of the ligand and in some cases about theinternal flexible structure of the protein. Since the number of possibleconformations is extremely large, it is not possible to test all of them.Therefore, sophisticated search techniques have to be applied.Examples of some commonly used methods are Genetic Algorithms and

Monte Carlo simulations.

2. An evaluation function, sometimes called a score function. This is afunction providing a measure of how strongly a given ligand will interacwith a particular protein. Energy force fields are often used asevaluation functions. These force fields calculate the energy contributiofrom different terms such as the known electrostatic forces between theatoms in the ligand and in the protein, forces arising from deformationof the ligand, pure electron-shell repulsion between atoms and effectfrom the solvent in which the interaction takes place.

It is not possible to guarantee that the search algorithm will find thesame solution as the true natural process, but more efficient search

algorithms will be more likely to find the true solution if the evaluationfunctions properly reflect the natural processes.

Metaphorically, the active site of the protein can be viewed as a lock,and the ligand can be thought of as a key. Molecular docking is theprocess of testing whether a given key fits a particular lock. Thisdescription is slightly oversimplified due to the fact that neither theligand nor the proteins are completely rigid structures. Their shapes aresomewhat flexible and may adapt to each other.


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Quantitative Structure-Activity Relationships (QSAR)

As mentioned in the previous paragraph it is necessary to know thegeometrical structure of both the ligand and the target protein in orderto use molecular docking methods. QSAR (Quantitative Structure-Activity Relationships) is an example of a method which can be appliedregardless of whether the structure is known or not.

QSAR formalizes what is experimentally known about how a givenprotein interacts with some tested compounds. As an example, it maybe known from previous experiments that the protein underinvestigation shows signs of activity against one group of compounds,but not against another group.

In terms of the lock and key metaphor, we do not know what the locklooks like, but we do know which keys work, and which do not. In orderto build a QSAR model for deciding why some compounds show sign ofactivity and others do not, a set of descriptors are chosen. These areassumed to influence whether a given compound will succeed or fail inbinding to a given target. Typical descriptors are parameters such as

molecular weight, molecular volume, and electrical andthermodynamical properties. QSAR models are used for virtualscreening of compounds to investigate their appropriate drug candidatedescriptors for the target.

Pharmacopoeia Mapping

Where QSAR focused on a set of descriptors like electrostatic andthermodynamic properties, Pharmacopoeia Mapping is a geometricalapproach. A pharmacophore can be thought of as a 3D model ofcharacteristic features of the binding site of the investigated protein(target). It may describe properties like: "In this region of the target apositive charge is needed, in this region there is a hydrogen donor, thatregion may not be occupied" and so on. On a pharmacophore model the

spheres indicate regions where a certain feature (e.g. a cation or ananion) is required. The pharmacophores are also used to define theessential features of one or more molecules with the same biologicalactivity.

Like QSAR models, pharmacophores can be built without knowing thestructure of the target. This can be done by extracting features fromcompounds which are known experimentally to interact with the targetin question. Afterwards, the derived pharmacophore model can be usedto search compound databases (libraries) thus screening for potentialdrug candidates that may be of interest.

2004-2010 Combinatorial Chemistry Review.


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In silicoFrom Wikipedia, the free encyclopedia

For other uses, see In silico (disambiguation).

In silico is an expression used to mean "performed on computer or via computer simulation." The phrase wascoined in 1989[citation needed]as an analogy to the Latin phrases in vivo and in vitro which are commonly used

in biology (see also systems biology) and refer to experiments done in living organisms and outside of living

organisms, respectively.

Contents

[hide]

1 Drug discovery with virtual screening

2 Cell models

3 Genetics

4 Other examples

5 History

o 5.1 In silico versus in silicio

6 See also

7 References

8 External links

[ ]Drug discovery with virtual screeningMain article: virtual screening

In silico research in medicine is thought to have the potential to speed the rate of discovery while reducing the

need for expensive lab work and clinical trials. One way to achieve this is by producing and screening drug

candidates more effectively. In 2010, for example, using the protein docking algorithm EADock (see Protein-

ligand docking), researchers found potential inhibitors to an enzyme associated with cancer activity in silico.

Fifty percent of the molecules were later shown to be active inhibitors in vitro.[1][2] This approach differs from use

of expensive high-throughput screening (HTS) robotic labs to physically test thousands of diverse compounds a

day often with an expected hit rate on the order of 1% or less with still fewer expected to be real leads following

further testing (see drug discovery).

[ ]Cell models

Efforts have been made to establish computer models of cellular behavior. For example, in 2007 researchers

developed an in silico model oftuberculosis to aid in drug discovery with a prime benefit cited as being faster


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than real time simulated growth rates allowing phenomena of interest to be observed in minutes rather than

months.[3]More work can be found that focus on modeling a particular cellular process like, for example, the

growth cycle ofCaulobacter crescentus.[4]

These efforts fall far short of an exact, fully predictive, computer model of a cell's entire behavior. Limitations in

the understanding of molecular dynamics and cell biology as well as the absence of available computer

processing power force large simplifying assumptions that constrain the usefulness of present in silico models.

[ ]Genetics

Digital genetic sequences obtained from DNA sequencing may be stored in sequence databases, be analyzed

(see Sequence analysis), be digitally altered and/or be used as templates for creating new actual DNA

using artificial gene synthesis.

[ ]Other examples

In silico computer-based modeling technologies have also been applied in:

Whole cell analysis of prokaryotic and eukaryotic hosts e.g. E. coli, B.

subtilis, yeast, CHO- or human cell lines

Bioprocess development and optimization e.g. optimization of product yields

Analysis, interpretation and visualization of heterologous data sets from

various sources e.g. genome, transcriptome or proteome data

[ ]History

The expression in silico was first used in public in 1989 in the workshop "Cellular Automata: Theory and

Applications" in Los Alamos, New Mexico. Pedro Miramontes, a mathematician from National Autonomous

University ofMexico (UNAM) presented the report "DNA and RNAPhysicochemical Constraints, Cellular

Automata and Molecular Evolution". In his talk, Miramontes used the term "in silico" to characterize biological

experiments carried out entirely in a computer. The work was later presented by Miramontes as

his PhD dissertation.[5]

In silico has been used in white papers written to support the creation of bacterial genome programs by the

C

ommission of the EuropeanC

ommunity. The first referenced paper where "in silico" appears was written bya French team in 1991.[6] The first referenced book chapter where "in silico" appears was written by Hans B.

Sieburg in 1990 and presented during a Summer School on Complex Systems at the Santa Fe Institute.[7]

The phrase "in silico" originally applied only to computer simulations that modeled natural or laboratory

processes (in all the natural sciences), and did not refer to calculations done by computer generically.

[ ]In silico versus in silicio


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CADASTER Seventh Framework Programme project aimed to develop in

silico computational methods to minimize experimental tests for

REACH Registration, Evaluation, Authorisation and Restriction ofChemicals

Journal ofIn Silico Biology

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