Docking (molecular)From Wikipedia, the free encyclopedia

Docking glossary

• Receptor or host – The "receiving" molecule, most commonly a protein or other biopolymer.

• Ligand or guest – The complementary partner molecule which binds to the receptor. Ligands are most often small molecules 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 the receptor as well as the conformation of the ligand and receptor when bound to each other.

• Pose – A candidate binding mode.

• Scoring – The process of evaluating a particular pose by counting the number of favorable intermolecular interactionssuch as hydrogen bonds and hydrophobic contacts.

• Ranking – The process of classifying which ligands are most likely to interact favorably to a particular receptor based on the predicted free-energy of binding.

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

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

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 “key” which 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-glove” analogy 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 receptor’s molecular surface is described in terms of its solvent-accessible surface area and the

ligand’s 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 protein’s 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.

[ ]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

protein’s 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 ligand’s 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 algorithmMain 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 space—this 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

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 functionMain 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]

[ ]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, and

docking 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): 402–6. doi:10.1016/S0959-

440X(96)80061-3. PMID 8804827.

2. ̂  Kitchen DB, Decornez H, Furr JR, Bajorath J (2004). "Docking and scoring

in virtual screening for drug discovery: methods and applications".Nature

reviews. Drug discovery 3 (11): 935–

49. doi:10.1038/nrd1549. PMID 15520816.

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

binding". Science 254 (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): 1161–82. 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 Chemistry 13 (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

empirical binding free energy function". Journal of Computational

Chemistry 19 (14): 1639–1662. doi:10.1002/(SICI)1096-

987X(19981115)19:14<1639::AID-JCC10>3.0.CO;2-B.

7. ^ a b 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 Chemistry 25 (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 Chemistry 13 (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". Bioinformatics 21 (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):

283–301. doi:10.1016/j.jmb.2007.01.086. PMID 17337005.

12. ̂  Kearsley SK, Underwood DJ, Sheridan RP, Miller MD (October 1994).

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

Comput. Aided Mol. Des. 8 (5): 565–

82. doi:10.1007/BF00123666. PMID 7876901.

13. ̂  Friesner RA, 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): 1739–49. 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 ONE 2 (9):

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

15. ̂  Klebe G, Mietzner T (October 1994). "A fast and efficient method to

generate biologically relevant conformations". J. Comput. Aided Mol.

Des. 8(5): 583–606. doi:10.1007/BF00123667. PMID 7876902.

16. ̂  Cerqueira NM, Bras NF, Fernandes PA, Ramos MJ (January 2009).

"MADAMM: a multistaged docking with an automated molecular modeling

protocol". Proteins 74 (1): 192–206. doi:10.1002/prot.22146. PMID 18618708.

17. ̂  Totrov M, Abagyan R (April 2008). "Flexible ligand docking to multiple

receptor conformations: a practical alternative". Curr. Opin. Struct. Biol.18 (2):

178–84. doi:10.1016/j.sbi.2008.01.004. PMID 18302984.

18. ̂  Hartmann C, Antes I, Lengauer T (February 2009). "Docking and scoring

with alternative side-chain conformations". Proteins 74 (3): 712–

26.doi:10.1002/prot.22189. PMID 18704939.

19. ̂  Taylor RD, Jewsbury PJ, Essex JW (October 2003). "FDS: flexible ligand

and receptor docking with a continuum solvent model and soft-core energy

function". J Comput Chem 24 (13): 1637–

56. doi:10.1002/jcc.10295. PMID 12926007.

20. ̂  Suresh PS, Kumar A, Kumar R, Singh VP (January 2008). "An in silico

[correction of insilico] approach to bioremediation: laccase as a case

study". J. Mol. Graph. Model. 26 (5): 845–

9. doi:10.1016/j.jmgm.2007.05.005. PMID 17606396.

[ ]External links

Bikadi Z, Kovacs S, Demko L, Hazai E. "Molecular Docking Server - Ligand

Protein Docking & Molecular Modeling". Virtua Drug Ltd. Retrieved 2008-07-

15. "Internet service that calculates the site, geometry and energy of small

molecules interacting with proteins"

Malinauskas T. "Step by step installation of MGLTools 1.5.2 (AutoDockTools,

Python Molecular Viewer and Visual Programming Environment) on Ubuntu

Linux 8.04". Retrieved 2008-07-15.

Malinauskas T. "High-throughput molecular docking using free tools: ZINC 8,

AutoDockTools 1.5.2 and Docker 1.0". Retrieved 2008-07-23.

AutoDock and MGLTools  for Debian

Docking@GRID  Project of Conformational Sampling and Docking on Grids :

one aim is to deploy some intrinsic distributed docking algorithms on

computational Grids, download Docking@GRID open-source Linux version

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

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

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 of cross-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.

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 Earth’s 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

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 from

marine 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 natural

products, 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

former approach is based on the fact that only a small part of Earth’s 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 Cancer Institute, 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 molecule’s architecture.

[ ]

[ ]References

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

Cardiotoxic Compounds", Genetic Engineering & Biotechnology News,

TechNote (Mary Ann Liebert) 29 (9): 34–35, 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 Discovery 9 (3): 203–

214.doi:10.1038/nrd3078. PMID 20168317.

3. ^ a b Feher M, Schmidt JM (2003). "Property distributions: differences

between drugs, natural products, and molecules from combinatorial

chemistry". J Chem Inf Comput Sci 43 (1): 218–

27. doi:10.1021/ci0200467. PMID 12546556.

4. ^ a b 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, Häbich 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". Nat Prod Rep 21 (1):

50–76. 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)

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

IUPHAR Committee 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 with target 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 for initiating clinical development. For the pharmaceutical industry, the number of years to bring 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 synthesizing compounds in a time-consuming multi-step processes against a battery of in vivo biological screens and further investigating the promising candidates for their pharmacokinetic properties, metabolism and potential toxicity. Such a development process has resulted in high attrition rates with failures attributed to poor pharmacokinetics (39%), lack of efficacy (30%), animal toxicity (11%), adverse effects in humans (10%) and various commercial and miscellaneous factors. Today, the process of drug discovery has been revolutionized with the advent of genomics, proteomics, bioinformatics and efficient technologies like, combinatorial chemistry, high throughput screening (HTS), virtual screening, de novo design, in vitro, in silicoADMET screening and structure-based drug design.What is in-silico Drug Design ?

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 with the target , rank them according to their binding affinites , further optimize the molecules to improve binding characteristics

The use of computers and computational methods permeates all aspects of drug discovery today and forms the core of structure-based drug design. High-performance computing, data management software and internet are facilitating the access of huge amount of data generated and transforming the massive complex biological data into workable knowledge in modern day drug discovery process. The use of complementary experimental and informatics techniques increases the chance of success in many stages of the discovery process, from the identification of novel targets and elucidation of their functions to the discovery and development of lead compounds with desired properties. Computational tools offer the advantage of delivering new drug candidates more quickly and at a lower cost. Major roles of computation in drug discovery are; (1) Virtual screening & de novo design, (2) in silicoADME/T prediction and (3) Advanced methods for determining protein-ligand binding

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Introduction to Drug DiscoveryDrug discovery and development is an expensive process due to the high costs of R&D and human clinical tests. The average total cost per drug development varies from US$ 897 million to US$ 1.9 billion. The typical 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 or activate the target. Clinical testing is the most extensive and expensive phase in drug development and is done in order to obtain the necessary governmental approvals. In the US drugs must be approved by the Food and Drug Administration (FDA).R&D – Finding the DrugOne of the most successful ways to find promising drug candidates is to investigate how the target protein interacts with randomly chosen compounds, which are usually a part of compound libraries. This testing is often done in so called high-thoughput screening (HTS) facilities. Compound libraries are commercially available in sizes of up to several millions of compounds. The most promising compounds obtained from the screening are called hits – these are the compounds that show binding 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 achieve more favorable interactions and less side-effects.Drug Discovery MethodsThe following are methods for finding a drug candidate, along with their pros and cons:1. Virtual screening (VS) based on the computationally inferred or simulated real screening;The main advantages of this method compared to laboratory experiments are:-low costs, no compounds have to be purchased externally or synthesized 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 to reduce the initial number of compounds before using HTS methods;-huge amount of chemicals to search from. The number of possible virtual molecules available for VS is exceedingly higher than the number

of compounds presently available for HTS;The disadvantage of virtual screening is that it can not substitute the real screening.2. The real screening, such as high-throughput screening (HTS), can experimentally test the activity of hundreds of thousands of compounds against the target a day. This method provides real results that are used for drug discovery. However, it is highly expensive.Virtual Screening in Drug DiscoveryComputational methods can be used to predict or simulate how a particular compound interacts with a given protein target. They can be used to assist in building hypotheses about desirable chemical properties when designing the drug and, moreover, they can be used to refine and modify drug candidates. The following three virtual screening or computational methods are used in the modern drug discovery process: Molecular Docking, Quantitative Structure-Activity Relationships (QSAR) and Pharmacopoeia Mapping.Molecular DockingWhen the structure of the target is available, usually from X-ray crystallography, the most commonly used virtual screening method is molecular docking. Molecular docking can also be used to test possible hypotheses before conducting costly laboratory experiments. Molecular docking programs predict how a drug candidate binds to a protein target. This software consists of two core components:1. A search algorithm, sometimes called an optimisation algorithm. The search algorithm is responsible for finding the best conformations of the ligand, a small drug-like molecule and protein system. A conformation is the position and orientation of the ligand relative to the protein. In flexible docking the conformation also contains information about the internal flexible structure of the ligand – and in some cases about the internal flexible structure of the protein. Since the number of possible conformations 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 a function providing a measure of how strongly a given ligand will interact with a particular protein. Energy force fields are often used as evaluation functions. These force fields calculate the energy contribution from different terms such as the known electrostatic forces between the atoms in the ligand and in the protein, forces arising from deformation of the ligand, pure electron-shell repulsion between atoms and effect from the solvent in which the interaction takes place.It is not possible to guarantee that the search algorithm will find the same solution as the true natural process, but more efficient search algorithms will be more likely to find the true solution if the evaluation functions 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 the process of testing whether a given key fits a particular lock. This description is slightly oversimplified due to the fact that neither the

ligand nor the proteins are completely rigid structures. Their shapes are somewhat flexible and may adapt to each other.Quantitative Structure-Activity Relationships (QSAR)As mentioned in the previous paragraph it is necessary to know the geometrical structure of both the ligand and the target protein in order to use molecular docking methods. QSAR (Quantitative Structure-Activity Relationships) is an example of a method which can be applied regardless of whether the structure is known or not.QSAR formalizes what is experimentally known about how a given protein interacts with some tested compounds. As an example, it may be known from previous experiments that the protein under investigation 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 lock looks like, but we do know which keys work, and which do not. In order to build a QSAR model for deciding why some compounds show sign of activity and others do not, a set of descriptors are chosen. These are assumed to influence whether a given compound will succeed or fail in binding to a given target. Typical descriptors are parameters such as molecular weight, molecular volume, and electrical and thermodynamical properties. QSAR models are used for virtual screening of compounds to investigate their appropriate drug candidates descriptors for the target.Pharmacopoeia MappingWhere QSAR focused on a set of descriptors like electrostatic and thermodynamic properties, Pharmacopoeia Mapping is a geometrical approach. A pharmacophore can be thought of as a 3D model of characteristic features of the binding site of the investigated protein (target). It may describe properties like: "In this region of the target a positive charge is needed, in this region there is a hydrogen donor, that region may not be occupied" and so on. On a pharmacophore model the spheres indicate regions where a certain feature (e.g. a cation or an anion) is required. The pharmacophores are also used to define the essential features of one or more molecules with the same biological activity.Like QSAR models, pharmacophores can be built without knowing the structure of the target. This can be done by extracting features from compounds which are known experimentally to interact with the target in question. Afterwards, the derived pharmacophore model can be used to search compound databases (libraries) thus screening for potential drug candidates that may be of interest. 

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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 was

coined 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 screening

Main 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

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 of Caulobacter 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 of Mexico (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

Commission of the European Community. The first referenced paper where "in silico" appears was written by

a 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

"In silico" was briefly challenged by "in silicio," which is correct Latin for "in silicon" (the Latin term for

silicon, silicium, was created at the beginning of the 19th century by Berzelius. Silex is also a correct latin

word).[citation needed] But the phrase "in silice" means "in flint" in Latin. "In silico" was perceived as catchier, possibly

through similarity to the words "vivo" and "vitro"[citation needed] "In silico" is now almost universal; it even occurs in a

journal title (In Silico Biology: http://www.bioinfo.de/isb/).

In silico is reasonable from the viewpoint of (ancient) Greek case endings; the "-on" ending for certain elements

is from Greek. In Greek, "silicon" would take the form "silico" in such a phrase. Latin typically uses the correct

Greek forms for Greek words when they are used in Latin.

[

[ ]References

1. ̂  Ludwig Institute for Cancer Research (2010) Rational Design of

Indoleamine 2,3-Dioxygenase Inhibitors. Journal of Medicinal Chemistry,

2010, 53 (3), pp 1172–1189 DOI: 10.1021/jm9014718

2. ̂  Ludwig Institute for Cancer Research (2010, February 4). New

computational tool for cancer treatment. ScienceDaily. Retrieved February

12, 2010,

from http://www.sciencedaily.com/releases/2010/01/100129151756.htm

3. ̂  University Of Surrey (2007, June 25). In Silico Cell For TB Drug Discovery.

ScienceDaily. Retrieved February 12, 2010,

fromhttp://www.sciencedaily.com/releases/2007/06/070624135714.htm

4. ̂  Li S, Brazhnik P, Sobral B, Tyson JJ, 2009 Temporal Controls of the

Asymmetric Cell Division Cycle in Caulobacter crescentus. PLoS Comput Biol

5(8): e1000463. doi:10.1371/journal.pcbi.1000463

5. ̂  Miramontes P. Un modelo de autómata celular para la evolución de los

ácidos nucleicos [A cellular automaton model for the evolution of nucleic

acids]. Tesis de doctorado en matemáticas. UNAM. 1992.

6. ̂  Danchin A, Medigue C, Gascuel O, Soldano H, Henaut A. From data banks

to data bases. Res Microbiol. 1991 Sep-Oct;142(7-8):913-6. PMID 1784830.

7. ̂  Sieburg, H.B. (1990). Physiological Studies in silico. Studies in the

Sciences of Complexity 12, 321-342.

[ ]External links

World Wide Words:  In silico

CADASTER  Seventh Framework Programme project aimed to develop in

silico computational methods to minimize experimental tests for

REACH Registration, Evaluation, Authorisation and Restriction of Chemicals

Journal of In Silico Biology

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