Computer aided drug design and bioinformatics: A current tool for designing
S. J. Daharwal
Drug design is an integrated developing discipline. It involves the study
of effects of biologically active compound on the basis of molecular interaction
in terms of molecular structure or its physiochemical properties involved.
The development of new methods in the field of molecular biology and computer
science, has improved the tools for drug design significantly. More and more
new drugs are developed with the help of computer technique.
The field of bioinformatics has become a major part of the drug design that
plays a key role for validation drug targets. Bioinformatics can help in understanding
of complex biological processes and help improve in understanding of complex
biological processes and help improve drug discovery.
KEY WORDS: CADD and Bioinformatics, Tool for designing of drug.
Drug design is an iterative process that begins when a chemist identifies a
compound that displays an interesting biological profile and ends when both
the activity profile and the chemical synthesis of the new chemical entity are
optimized. In general, clinically used drugs are not discovered. The compound
likely discovered as lead compound. The lead is a prototype compound that has
a desired biological or pharmacological activity but may have many undesirable
characteristics eg. high toxicity, insolubility etc. For designing of
drug there are two types of hypothesis viz. Drug discovery with out lead and
Traditional approaches to drug discovery rely on a step-wise synthesis and screening program for large numbers of compounds to optimize activity profiles. Nobody could design a drug before knowing more about the disease or infectious process than past. For "rational" design, the first necessary step is the identification of a molecular target critical to a disease process or an infectious pathogen. Then the important prerequisite of "drug design" is the determination of the molecular structure of target, which makes sense of the word “rational”.
Drug discovery without a lead.
The examples of drug discovery without a lead are quit few in no. The typical
occurrence is that a lead compound is identified & its structure is modified
to give, eventually, the drug that goes to the clinic. e.g. Penicillin and Librium.
There are several approaches that can be taken to identify a lead. The first requirement to all of the approaches is to have a meant to assay compounds for particular biological activity. Bioassay (or screen) is a means of determining in a biological system, relative to a controlled compound, whether a compound has a desired activity and if so, what the relative potency of the compound is. The pharmacological is depends upon the activity and potency of drug. Activity is a particular biological or pharmacological effect. e.g. antibacterial activity. Potency is the strength of that effect.
Drugs work by interacting with
target molecules (receptors) in our bodies and altering their activities in
a way that is beneficial to our health. In some cases, the effect of a drug
is to stimulate the activity of its target (an agonist) while in other cases
the drug blocks the activity of its target (an antagonist).
Finding effective drugs is difficult.
Many are discovered by chance observations, the scientific analysis of folk
medicines or by noting side effects of other drugs. A more systematic method
is large-scale screening experiments where potential drug targets are tested
with thousands of different compounds to see if interactions take place.
Random Screening involves no intellectualization; all compounds are tested in the bioassay without regard to their structure. Random Screening programme are still very important in order to discover drugs or leads that have unexpected and unusual structures for various targets. e.g. antibiotics streptomycin, tetracycline.
Non random screening
It is a slightly narrow approach than the random screening. In this case compounds having a vague resemblance to weakly active compounds uncovered in a random screen or compounds containing different functional groups than leads may be tested selectively.
Drug metabolism studies
During metabolism studies, drug metabolites (drug degradation products generated in vivo) that are isolate are screened in order to determine. If the activity is observed is derived from the drug candidate or from metabolites.
Often a drug candidate during animal testing or clinical trials will exhibit more than one pharmacological activity that is it may produce a side effect. This compound then can be used as lead for secondary activity.
e.g. Carbutamide ------------- antidiabetic
(antibacterial) (side effect or drug)
Rational approach to drug discovery
The rational approaches are directed at lead discovery. It is not possible, with much accuracy to foretell toxicity and side effects, anticipate transport of a drug. Once a lead is identified its structures can be modified until an effective drug is prepared.
Rational drug design is a more focused approach, which uses information about
the structure of a drug receptor or one of its natural ligands to identify or
create candidate drugs. The three-dimensional structure of a protein can be
determined using methods such as X-ray crystallography or nuclear magnetic resonance
Armed with this information, researchers in the pharmaceutical industry can use powerful computer programme to search through databases containing the structures of many different chemical compounds. The computer can select those compounds that are most likely to interact with the receptor, and these can be tested in the laboratory.
If an interacting compound cannot be found in this manner, other programme could be used that attempt, from first principles, to build molecules that are likely to interact with the receptor. Further programme can search databases to identify compounds with similar properties to known ligands. The idea is to narrow down the search as much as possible to avoid the expense of large-scale screening.
The first drug produced by rational design was Relenza, which is used to treat influenza. Relenza was developed by choosing molecules that were most likely to interact with neuroaminidase, a virus-produced enzyme that is required to release newly formed viruses from infected cells.
Many of the recent drugs developed to treat HIV infections (e.g. Ritonivir, Indinavir) were designed to interact with the viral protease, the enzyme that splits up the viral proteins and allows them to assemble properly.
Another well-known drug that was produced by ligand-based design is Viagra. This drug was designed to resemble cGMP. A ligand that binds an enzyme called phosphodiesterase. By blocking phosphodiesterase activity, it was hoped that the drug would help to relax the vascular smooth muscle in the heart and therefore relieve the symptoms of angina. In clinical trials, the effect of the drug on angina was not encouraging, but some of the male patients developed erections.
Drug development: lead modification
It is used for the to modify in order to improve desired pharmacological properties.
· Identification of an active part: The pharmacophore
Only a small part of a lead compounds may be involved in the appropriate interaction. The relevant groups on a molecule that interact with the receptor and are responsible for activity are collectively known as pharmacophore.
· Functional group modification
There is a obvious relationship between the molecular structure of a compounds and its activity. By modifying a functional group by replacement with other functional group the activity of compound get charged. e.g. the amino group of carbutamide was replaced by methyl group to give tolbutamide and in so doing the antibacterial activity was separated away from ant diabetic activity.
Structure activity relationship
In 1868 Crum brown and Fraser suspecting that the quaternary ammonium character
of curare may be responsible for its muscular paralytic properties examined the
neuromuscular blocking effects of variety of simple quaternary salts and quternised
alkaloid in animal. From these studies they conclude that the physiological action
of a molecule was the function its chemical constituent. Shortly therefore Richardson
noted that the hypnotic activity of aliphatic alcohol was the function of their
molecular weight. These observations are the basis for future structure activity
relationship (SAR). Drug can be classified as being structurally specific or structurally
nonspecific. Structurally specific drug
These are the drug act at specific site such as a receptor or an enzyme. Their activity and potency are very susceptible to small change in chemical structure; molecules with similar biological activities tend to have common structural feature.
Structurally nonspecific drug
It has no specific site of action and usually has lower potency. Similar biological activities may occur with a variety of structure. e.g. Gaseous anesthetics, sedative and hypnotics.
Sulfonamide diuretics are two general structure types, hydrochlorthiazides
and the high ceiling type. The former compound has 1,3-disulfamyle groups on
the benzene ring and R2 is an electronegative group such as Cl, CF3
, NHR. The high ceiling compound contain1-sulfamyl-3-corboxy group substituents
R2 is Cl , Ph, PhZ. Where Z may be O, S, or NH and X can be position
2 or 3 and is normally NHR, OR, or SR. These are the drugs that act at specific
sites. Any structural modification can increase potency and therapeutic index.
Therapeutic index or therapeutic ratio is the measure of ratio of undesirable
drug effect. For in vivo systems the therapeutic index could be ratio of the
LD50 (the lethal dose for the 50% of test animal) to the ED50 (the effective
dose that produce the maximum therapeutic effect in 50% of the test animal)
It is a series of groups of compounds that differs by a constant unit generally a –CH2 group.
When a simple lipophilic relationship is important then chain branching lowers the potency of compounds. Chain branching also can interfere with receptor binding.
Ring chain transformation
Different activity can result from a ring –chain transformation. e.g. If the
dimethyl amino group of chlorpromazine is substituted by a methyl piperazine
ring the antiemetic (prevents nausea and vomiting) actively is greatly enhanced.
Bioisosteres are substituents or group that have chemical and physical similarities, which produce broadly similar biological properties. These have following types.
They have same number of atom and fit the steric and electronic rules and produce similarity in biological activities.
Univalent atom and groups
CH3, NH, OH, F , Cl
Cl, PH 2 SH
Bivalent atom and group
CH2 , NH , O, S, Se
_COCH2R, _CONHR _CO2R _COSR
They not have the same number of atoms and do not fit the steric and electronic rule of classical isosteres but they do produce a similarity in biological activity.
H, , F
X CF3 CN
Quantitative Structure-Activity Relationships (QSAR)
Quantitative structure-activity relationships (QSAR) represent an attempt to correlate structural or property descriptors of compounds with activities. These physicochemical descriptors, which include parameters to account for hydrophobicity, topology, electronic properties, and steric effects, are determined empirically or, more recently, by computational methods. Activities used in QSAR include chemical measurements and biological assays. QSAR currently are being applied in many disciplines, with many pertaining to drug design and environmental risk assessment.
The QSAR approach is a rational approach to lead optimization when the structure of the target is not known. The underlying premise of QSAR is that there is a relationship between the biological and pharmacological activity of a compound, and its structural, physical and chemical properties. i.e. Activity is a function of Structure and an equation can be determined relating activity (not yet determined) to parameters that can be determined, for example, by computer. An important advantage of QSAR is that it models the in vivo situation since it is based on activity data.
This approach allows important structural requirements for activity to be identified and hence narrows the search for the optimum molecule. Since it is a quantitative measure, how much each aspect alters activity can also be determined. QSAR equations, once determined, also allow new compounds activity to be predicted based on structural data and hence saves time synthesizing molecules unlikely to have good activity.
A congeneric series of compounds
This is a series of compounds with a similar basic structure but with varying substituents. Hence are known to act on the same target in the same manner. An example of an antifungal base structure used for QSAR studies by varying R and R'.
Some quantitative activity data for this series of compounds.
This data is generated experimentally, assuming that all compounds have the same mode of action. ie kcat data for enzyme and measuring receptor activation through measurement of molecules known to arise from that receptor's signal transduction pathway.
Some quantitative chemical, physical or structural parameters need to be known for the varying substituents.
Since the structures only vary with respect to certain substituents, only the structural parameters of these substituents need to be known. Each substituent is described by a substituent constant, which quantifies a particular aspect of structure. Eg.
-Hammett Electronic Substituent Constant (sigma): quantifies the electron withdrawing or electron donating effect to a substituent. -Taft steric substituent constant (Es): quantifies these substituent size relative to methyl group.- Hansch/Fujita hydrophobicity substituent constant (Pi): measures the contribution a substituent makes to the total partition coefficient of the molecule.-Charge on certain atoms.
A powerful computer capable of performing complex statistical analysis.
The activity is usually dependent on more than one aspect of structure. This
will be seen if a non-linear plot arises from Activity vs. Hammett Electronic
Substituent Constant. A commonly used technique is multiple linear regressions
that plot each substituent constant on a multidimensional graph, against activity.
A line of best fit is then found in multiple dimensions with a corresponding
Regression Coefficient (R2). The regression coefficient indicates
how well the points fit the proposed line. R2 lies between -1 and
1, with a value of -1 or 1 indicating that all the variation on the line can
be explained. If R2 lies close to 0, no variation observed can be
explained by the variation in substituent constants. In order to be statistically
significant, the original group of congeneric molecules should number at least
5. If the substituent constants are highly correlated or are very numerous,
it is possible to exploit a partial least squares method, which expresses a
Comparative Molecular Field Analysis (CoMFA)
CoMFA is a very widely used technique that is an extension of the QSAR approach.
CoMFA, like QSAR is based on a congeneric series of molecules. These molecules
are overlaid so their common structures overlap and they are all in their conformation
suited to optimum activity. The molecular field of each molecule is then calculated
by placing the overlapped molecules in a 3D grid and using regularly placed
probes to measure the molecular field. Partial least squares is used to calculate
the QSAR since there are millions of possible grid points but only a limited
number of molecules overlapped. It is possible to test the predictive quality
of the CoMFA through cross validation (Q2), whereby one molecule
at a time is omitted from the CoMFA analysis and is then used to test the activity
prediction. The result of a CoMFA is a 3D grid, which connects points with similar
influence on activity. This can be used to identify regions where increasing
or decreasing a substituent constant would influence activity. McGaughey et
al have used the CoMFA technique in the search for novel dopamine agonists at
the D2 receptor. In PD, the D2 receptor is important pre-synaptically
for neuroprotective effects and post synaptically for anti-parkinsonian effects.
A number of favorable properties were identified. Based on this analysis, the
activity of a series of test molecules was predicted and subsequently verified
experimentally to be accurate. It is also possible to confer possible binding
mechanisms through examination of favorable steric CoMFA fields.
Isolate and purify or design synthesis
If the lead compound derives from an abundant natural source, it may be easier to isolate and purify the compound for further drug development. Given the large potential market for a new drug however, a synthetic chemical route is usually preferable if possible in order to conserve natural resources and to control purity and yield. If complete synthesis is impossible however, an important precursor may be isolated and purified for further use (as was necessary and achieved in the wonderful story of isolation of a precursor semi synthesis of taxol)
This approach to CADD optimizes the fit of a ligand in a receptor site.Based on the information that is available, one can apply either ligand-based or receptor-based molecular design methods. The ligand-based approach is applicable when the structure of the receptor site is unknown, but when a series of compounds have been identified that exert the activity of interest. To be used most effectively, one should have structurally similar compounds with high activity, with no activity, and with a range of intermediate activities. In recognition site mapping, an attempt is made to identify a pharmacophore, which is a template derived from the structures of these compounds. It is represented as a collection of functional groups in three-dimensional space that is complementary to the geometry of the receptor site
One of the basic tenets of medicinal chemistry is that biological activity is dependent on the three-dimensional placement of specific functional groups (the pharmacophore).
The development of molecular modeling programs and their application in pharmaceutical research has been formalized as a field of study known as computer assisted drug design (CADD) or computer assisted molecular design (CAMD).
Computational chemistry/molecular modeling is the science (or art) of representing molecular structures numerically and simulating their behavior with the equations of quantum and classical physics. Computational chemistry programs allow scientists to generate and present molecular data including geometries (bond lengths, bond angles, torsion angles), energies (heat of formation, activation energy, etc.), electronic properties (moments, charges, ionization potential, electron affinity), spectroscopic properties (vibrational modes, chemical shifts) and bulk properties (volumes, surface areas, diffusion, viscosity, etc.).
In applying this approach, conformational analysis will be required,
the extent of which will be dependent on the flexibility of the compounds under
investigation. One strategy is to find the lowest energy conformers of the most
rigid compounds and superimpose them. Conformational searching on the more flexible
compounds is then done while applying distance constraints derived from the
structures of the more rigid compounds. Ultimately, all of the structures are
superimposed to generate the pharmacophore.
Enzymes take a key role in the research of the pharmaceutical industry, because
they represent targets for the specific development of drugs. Within the scope
of rational drug design computational methods gain more and more importance
to design workflows that are faster, more efficient and cheaper. A main principle
in drug discovery and development is the interaction between receptors and enzymes
with their ligands.
It is the study of drug effects and attempts to elucidate the complete action effect sequence and the dose effect relationship.
Principle of drug action
The basic type of drug action can be broadly classed as:
Stimulation : It is selectively enhancement of the level of activity
of specialized cells.
E.g. .adrenaline stimulate the heart.
Depression: It is selectively diminution of activity of specialized cell. e.g. Barbiturate depresses CNS.
Irritation: It is particularly applied to less specialized cell (epithelium, connective tissue). Mild irritation and strong irritation are the two types of irritation. Mild irritation,it may stimulate associated function eg.Bitter increase salivary and gastric secretion. Strong irritation, it results in inflammation, corrosion, necrosis and morphological damage.
Replacement: This refers to use of natural metabolites, hormones
or their congeners in deficiency. e.g. Levodopa in Parkinson’s diseases.
Cytotoxic action: Selection cytotoxic action for invading parasites
and cancer cells. Palliation of infection and neoplasm. e.g. Penicillin, chloroquine.
Mechanism of drug action: The fundamental mechanism of drug action can be distinguished into four categories.
· Physical properties: Physical properties are responsible for its action.
e.g. Mass of drugs: bulk laxative (bran)
· Adsorptive property: Charcoal, kaolin.
· Chemical action: The drug reacts extracellularly according to simple chemical
e.g.. Antacid neutralized gastric HCl.
·Through enzyme: All most all-biological reactions carried out under
catalytic influence of enzyme:
·Stimulation: Enzyme stimulations is relevant to many endogenous mediator
and modulators. e.g.. Adrenaline stimulates adenylyl cyclase.
·Inhibition :Inhibition of enzyme a common mode of drug action.
·Non-specific inhibition: Many chemicals and drugs are capable of denaturating
protein. e.g.. heavy metals ,phenols etc.
·Specific inhibition: Many drugs inhibit a particular enzyme with affecting
others. e.g.. Waferine competes with vit. K, which acts as a co-enzyme for enzyme
synthesizing clotting factor in liver.
Receptor is a specific binding site with functional co-relates.
Agonist activates receptors
to produce an effect similar to that of physiological signal molecules.
the action of agonist.
Inverse agonist activates a receptor to produce an effect in the opposite direction to the agonist.
Partial agonist activates a receptor to produce sub maximal effects but antagonize the action agonist.
Ligand is a molecule, which attach selectively to particular receptor or site.
It provides indications of substructures relevant for the receptor affinity
of the different substrates and leads to indirect mapping of the receptor site.
This pharmacophore pattern can be derived from the 3D maximum common substructure
(3D-MCSS) that these compounds have in common and it can help finding new lead
structures necessary for drug design in medicinal chemistry (Figure 1). In addition
to the structural similarity, which is calculated through the MCSS, there is
also a necessity for similarity in physicochemical properties of the molecules
for the estimation of molecular recognition.
Drug specificity and side effects
An important criterion to determine the medical value of a drug is specificity: the physiological effect of the drug should be as clearly defined as possible. It has to specifically bind to the target protein in order to minimize undesired side-effects
Proteins as cellular targets of medical drugs
The cellular targets (or receptors) of many drugs used for medical treatment are proteins. By binding to the receptor, drugs either enhance or inhibit its activity. Basically there are two major groups of receptor proteins: proteins that "float" around in the cytoplasm of the cell, and proteins that are incorporated into the cell membrane. In the latter case, a drug does not even need to enter the cell; it can bind simply to an extra cellular binding site of the protein and control intracellular reactions from the outside.
The molecular basis of drug specificity
On the molecular level specificity includes two more or less independent mechanisms: first the drug has to bind to its receptor site with a suitable affinity (better binding means lower doses) and second it has to either stimulate or inhibit certain movements of the receptor protein in order to regulate its activity. Both mechanisms are mediated by a variety of interactions between the drug and its receptor site.
In all cases, the aim of using the computer for drug design is to analyze the interactions between the drug and its receptor site and to "design" molecules that give an optimal fit.
Rational drug design can be performed based on a known enzyme or receptor-binding site. From the 3-dimensional structure of the site a pharmacophore can be determined; that pharmacophore may then be used as the basis for the de novo design of novel ligands for that receptor. Since the structure of enzyme or receptor active sites is complex we will substitute a model receptor-binding site: a Rebek’s diacid. In the following problem you will analyze a modified Rebek’s diacid, develop a pharmacophore based on the receptor structure and design a ligand that reproduces the features of the pharmacophore and, therefore, should bind to the receptor
De Novo Ligand Design
(A) Based on the pharmacophore from Part 2 design a novel ligand that will include the features of the pharmacophore from Part 2 and, therefore, bind to the unknown receptor.
I. Energy minimize the novel ligand using ChemSite
ii. Check that rotatable dihedrals are near their energy minima
B) Print out the molecule alone
i. Label important atoms/functional groups
ii. Label distances related to pharmacophore
C) Describe how well the new compound fulfills the pharmacophore presented in Part 2
I. Discuss specific interactions and distances
ii. Discuss possible limitations of the compound
iii. Discuss issues associated with the solubility of your drug
D) Print out the molecule docked in the receptor-binding site in two orientations
I. Front view of drug bound to the receptor
ii. Side view of the drug bound to the receptor to make sure that the drug is in the plane of the receptor.
iii. Present the drug-receptor interaction energy
Criteria for grading
1) Quality of analysis of receptor
2) Validity of pharmacophore and justification with respect to receptor
3) Design of appropriate ligand and justification/limitations of that compound
4) General Presentation
The design of the ligand is based only on the pharmacophore and not on docking
of the ligand into the receptor, although an image of the ligand bound to the
receptor is to be included in the report. Omission of the docking step is based
on the difficulty of doing the docking and the limitation that our receptor
is rigid. In practice the flexibility of the receptor would make it much easier
to locate that ligand in the receptor and obtain favorable interaction energy.
To take into account the flexibility of the receptor, assume that all atoms
can move ±0.5 Å.
A structure drawn in a drawing package will not have an optimum 3D geometry.
It is possible to find the optimum geometry by rotating bonds, moving groups
closer of further away from one another, shortening bond angles etc. This is
obviously a time consuming process making modifications and then measuring the
energy of the molecules so computer programs such as Mopac and Gamess have been
designed to optimize geometry.
The researchers will exploit all of the possible approaches to design or find good candidates for drug. Pharmaceutical researchers identify possible lead compounds from a variety of sources:-Natural world (marine biology, bacteria, plants).
-Screening banks of compounds against any target information available.
-Enhancing aside effect. Start from the natural ligand or modulator. For example, with dopamine, we need to identify pharmacophoric groups on the natural agonist.
Direct ligand design
If the structure of the target enzyme or receptor is known, it is possible to directly design a ligand. Initially, important binding and interactive areas need to be identified. Groups are then chosen which will interact well with important areas. E.g. - Hydrophobic region will suit a benzene group on the proposed ligand. - H bonding could be encouraged with an NH2 substituent.
The processes of designing a new drug using bioinformatics tools have opened a new area of research. Using computational methods such as molecular docking and homology modeling and experimental methods such as NMR spectroscopy, X-ray crystallography and biological assays, it is possible to screen library of compounds towards protein targets and to solve the three-dimensional structure of a protein: inhibitor complex. Specifically, such techniques allow designing and discovering novel inhibitors with enhanced selectivity towards a particular receptor, therefore limiting side effects and toxicity.
Bioinformatics and molecular simulations are theoretical tools, which more
and more increasingly play an important role in the drug design process. Informatics-based
approaches result in a powerful process for rapid structure-based lead generation
In order to design a new drug one need to follow the following path.:
Identify target disease
One needs to know all about the disease and existing or traditional remedies. It is also important to look at very similar afflictions and their known treatments.
Target identification alone is not sufficient in order to achieve a successful treatment of a disease. A real drug needs to be developed. This drug must influence the target protein in such a way that it does not interfere with normal metabolism. One way to achieve this is to block activity of the protein with a small molecule. Bioinformatics methods have been developed to virtually screen the target for compounds that bind and inhibit the protein. Another possibility is to find other proteins that regulate the activity of the target by binding and forming a complex.
Study interesting compounds
One needs to identify and study the lead compounds that have some activity against a disease. These may be only marginally useful and may have severe side effects. These compounds provide a starting point for refinement of the chemical structures.
Detect the molecular bases for disease
If it is known that a drug must bind to a particular spot on a particular protein or nucleotide then a drug can be tailor made to bind at that site.
Rational drug design techniques:
These techniques attempt to reproduce the researchers' understanding of how to choose likely compounds built into a software package that is capable of modeling a very large number of compounds in an automated way
Refinement of compounds:
Once you got a number of lead compounds have been found, computational and laboratory techniques have been very successful in refining the molecular structures to give a greater drug activity and fewer side effects. This is done both in the laboratory and computationally by examining the molecular structures to determine which aspects are responsible for both the drug activity and the side effects.
Quantitative Structure Activity Relationships (QSAR):
This computational technique should be used to detect the functional group in your compound in order to refine your drug. This can be done using QSAR that consists of computing every possible number that can describe a molecule then doing an enormous curve fit to find out which aspects of the molecule correlate well with the drug activity or side effect severity. This information can then be used to suggest new chemical modifications for synthesis and testing.
Solubility of Molecule:
One need to check whether the target molecule is water soluble or readily soluble in fatty tissue will affect what part of the body it becomes concentrated in. The ability to get a drug to the correct part of the body is an important factor in its potency. Ideally there is a continual exchange of information between the researchers doing QSAR studies, synthesis and testing. These techniques are frequently used and often very successful since they do not rely on knowing the biological basis of the disease which can be very difficult to determine.
Once a drug has been shown to be effective by an initial assay technique, much more testing must be done before it can be given to human patients. Animal testing is the primary type of testing at this stage. Eventually, the compounds, which are deemed suitable at this stage, are sent on to clinical trials. In the clinical trials, additional side effects may be found and human dosages are determined.
There are several key areas where bioinformatics supports CADD research.
Another common challenge in CADD research is determining the 3-D structure of proteins. Most drug targets are proteins, so it’s important to know their 3-D structure in detail. It’s estimated that the human body has 500,000 to 1 million proteins. However, the 3-D structure is known for only a small fraction of these. Homology modeling is one method used to predict 3-D structure. In homology modeling, the amino acid sequence of a specific protein (target) is known, and the 3-D structures of proteins related to the target (templates) are known. Bioinformatics software tools are then used to predict the 3-D structure of the target based on the known 3-D structures of the templates
A common activity in biopharmaceutical companies is the search for drug analogues. Starting with a promising drug molecule, one can search for chemical compounds with similar structure or properties to a known compound. There are a variety of methods used in these searches, including sequence similarity, 2D and 3D shape similarity, substructure similarity, electrostatic similarity and others
Drug Lead Optimization:
When a promising lead candidate has been found in a drug discovery program, the next step (a very long and expensive step!) is to optimize the structure and properties of the potential drug. This usually involves a series of modifications to the primary structure (scaffold) and secondary structure (moieties) of the compound. This process can be enhanced using software tools that explore related compounds (bio-isosteres) to the lead candidate. Lead optimization tools such as WABE offer a rational approach to drug design that can reduce the time and expense of searching for related compounds.
Drug-receptor interactions occur on atomic scales. To form a deep understanding of how and why drug compounds bind to protein targets, we must consider the biochemical and biophysical properties of both the drug itself and its target at an atomic level.
Drug Bioavailability and Bioactivity.
Most drug candidates fail in Phase III clinical trials after many years of research and millions of dollars have been spent on them. And most fail because of toxicity or problems with metabolism. The key characteristics for drugs are Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) and efficacy—in other words bioavailability and bioactivity. Although these properties are usually measured in the lab, they can also be predicted in advance with bioinformatics software.
Benefits of CADD
CADD methods and bioinformatics tools offer significant benefits for drug discovery
- Cost Savings.
It suggests that the cost of drug discovery and development has reached $800 million
for each drug successfully brought to market. Many biopharmaceutical companies
now use computational methods and bioinformatics tools to reduce this cost burden.
Virtual screening, lead optimization and predictions of bioavailability and bioactivity
can help guide experimental research. Only the most promising experimental lines
of inquiry can be followed and experimental dead-ends can be avoided early based
on the results of CADD simulations.
The predictive power of CADD can help drug research programs choose only the most promising drug candidates. By focusing drug research on specific lead candidates and avoiding potential “dead-end” compounds, biopharmaceutical companies can get drugs to market more quickly.
One of the non-quantifiable benefits of CADD and the use of bioinformatics tools is the deep insight that researchers acquire about drug-receptor interactions. Molecular models of drug compounds can reveal intricate, atomic scale binding properties that are difficult to envision in any other way. When we show researchers new molecular models of their putative drug compounds, their protein targets and how the two bind together, they often come up with new ideas on how to modify the drug compounds for improved fit. This is an intangible benefit that can help design research programs therefore CADD and bio-informatics together is a powerful combination in drug research and development.
Modern chemistry techniques and practices allow for the generation of a vast number of compounds and an even greater amount of data. As a result, one of the biggest challenges facing chemists today is cataloging all of the available information so they can analyze it and learn from it. The new field of chemo informatics provides an invaluable tool in these efforts.
Chemo informatics refers to the building of electronic databases of the chemical and biological properties and effects of large numbers of chemical compounds. These databases allow graphic and other visual information about chemical structures to be directly linked with the very large amount of both raw and processed data provided by medicinal, pharmaceutical, and other fields of research.
Use chemoinformatic databases to allow the mining of very large datasets of chemical and biological information to look for new drug leads or drug design ideas, thereby speeding the discovery and development of new drugs.
The ability synthesis and to assay vastly greater number of molecules than even a few years ago requires tool to rationalized the resulting structural and biological data.
- Scale up the previous work (new algorithms and faster hard work)
- Development of new methods (random/rational argument)
- Molecular diversity analysis.
- Virtual screening
Diversity: Current molecular diversity analysis, structurally
similarly molecule will tend to have a same properties. Considerations of cost-effectiveness
suggest needing the focus, initially at least on structurally divers set of
- Identifying appropriate structures representation
- Selecting set of molecule
- Quantifying diversity.
Virtual screening: Need to priorities the many molecules that
could be tested.
- Increasingly sophisticated level of filtering to maximize the number of
- Drug ability consideration.
- Similarly searching (both 2D and 3D) using initial weak leads.
- 3D sub structure searching once opposable pharmacophoric patter has been
- Docking once the 3D structures of the biological target is available.
A very important part of drug design is prediction of small molecule binding to a target macromolecules .De novo drug design is an iterative process in which the three dimensional structure of the receptor is used to design a newer molecules. It involves structure determination of lead target complexes and the design of the lead modification using different tools. It can also be used to design new chemical classes of compounds that present similar substituent to the target.
Pharmaceutical drug development using computer design technique inherently requires complex drug design. Drugs vary from the simple to complex but the receptor are usually extremely complex drug development is further complicated by the fact that it is a bimolecular system composed of biological receptor and ligand (drug or protein).
- Jerzy Cioslowski, in Reviews in Computational Chemistry, VCH Publishers, 1991, vol. 2, Kenny B. Lipkowitz and Donald B. Boyd (editors), pages 1-55.
- D.R. Ripoll, C.H. Faerman, P.H. Axelsen, I. Silman and J.L. Sussman, An Electrostatic Mechanism for Substrate Guidance Down the Aromatic Gorge of Acetylcholinesterase, , Proc. Nat. Acad. Science, 90, 5128-5132, 1993.
- Black, Shaun D.; Mould, Diane R,. Development of Hydrophobicity Parameters
to Analyze Proteins Which Bear Post- or Cotranslational Modifications, Analytical
Biochemistry 1991, 193, 72-82
- Bravi, G.; Gancia, E.; Zaliani, A, MS-WHIM, new 3D theoretical descriptors derived from molecular surface properties: A comparative 3D QSAR study in a series of steroids, Journal of computer-aided molecular design 1997, 11(1), 79
- Bruce R. Gelin and Martin Karplus, PNAS, 74, 801-805, 1977.
- Roderick E Hubbard, Can drugs be designed? [Review article], Current
Opinion in Biotechnology 1997, 8:696-700.
- In Reviews in Computational Chemistry, VCH Publishers, 1990, vol. 1, Kenny B. Lipkowitz and Donald B. Boyd (editors), pages 295-320.
- Conformation Space Mapping - A Novel Method for Structure Based Drug Design. Oren Becker Folding & Design 1996, 1:A16.
- Conformational Analysis, Ernest Eliel, et. al., Wiley Interscience, 1965.
- .A. Dammkoehler, S.F. Karasek, E.F.B. Shands and G.R. Marshall, Constrained Search of Conformational Hypersurface, J. Comput-Aided Mol. Design, 3, 3, 1989.
- Michael P Morrissey, Eugene I Shakhnovich, Design of proteins with selected thermal properties. Folding & Design 20 Sep 1996, 1:391-405.
- Des Jarlais, R.L., Sheridan, R.P., Seibel, G.L., Dixon, J.S., Kuntz, I.D., and Venkataraghavan, R. Using Shape Complementarily as an Initial Screen in Designing Ligands for a Receptor Binding Site of Known Three-Dimensional Structure. J. Med. Chem., 31: 722-729.
- V .M. Kulkarni and K . G. Bothra, Drug design, Nirali publication.
- Jonathan B Chaires, Drug-DNA interactions. [Review article], Current Opinion in Structural Biology 1998, 8:314-320.
- C.R. Robinson and S.G. Sligar, Electrostatic Stabilization in Four-Helix Bundle Proteins, , Protein Science 2, 826-837, 1993.
- Estrada, Ernesto; Ramirez, Alain, Edge Adjacency Relationships and Molecular Topographic Descriptors. Definition and QSAR Applications J. Chem. Inf. Comput. Sci. 1996, 36(4), 837
- Rebecca C Wade, 'Flu' and structure-based drug design [Mini review], Structure 1997, 5:1139-1145.
- G.R. Marshall, C.D. Barry, H.E. Bosshard, R.A. Dammkoehler and D.A. Dunn, Computer Assisted Drug Design, ACS Symposium Series 112, American Chemical Society, Washington DC, 1979.
- Gao, Hua; Hansch, Corwin QSAR of P450 Oxidation: On the Value of Comparing Kcat and Km with Kcat/Km. Drug Metabolism Reviews 1996, 28(4), 513-526
- Genetic Algorithms: Principles of Natural Selection Applied to Computation, Stephanie Forrest, Science, 261, 872-878, 1993.
- Hansch, C., A Quantitative Approach to Biochemical Structure-Activity Relationships. Acct. Chem. Res. 1969, 2: 232-239.
- Hansch, C. Drug Research or the Luck of the Draw. J. Chem. Ed., 1974, 51: 360-365.
- Hansch, C., The QSAR Paradigm in the Design of Less Toxic Molecules. Drug Metab. Rev., 1984-85, 15: 1279-1294.
- Hansch, C., Leo, A., and Taft, R.W., A Survey of Hammett Substituent Constants and Resonance and Field Parameters. Chem. Rev., 1991, 91: 165-195.
- Karelson, Mati; Lobanov, Victor S.; Katritzky, Alan R., Quantum-Chemical Descriptors in QSAR/QSPR Studies Chemical reviews 1996, 96(3),1027
- Katrizky, Alan P.; Gordeeva, Elkaterina V., Traditional Topological Indices vs. Electronic, Geometrical, and Combined Molecular Descriptors in QSAR/QSPR REsearch, J. Chem. Inf. Comput. Sci. 1993, 33(6), 835
- KS Athai , UV Singh, K Satyanarayan and N Udupa Indian Drugs, 1998, 60(2), 68
- Lorman, S., New QSAR Techniques Eyed for Environmental Assessments. Chem. Eng. News, 1990, 68: 20-23.
- Maddalena, Desmond J.; Johnston, Graham A. R., Prediction of Receptor Properties and Binding Affinity of Ligands to Benzodiazepine/GABAA Receptors Using Articial Neural Networks, J. Med. Chem. 1995, 38, 715-724
- A.W.R. Payne and R.C. Glen, Molecular recognition using a binary genetic search algorithm, , J. Mole. Graphics, 1993, 11, 74-91
- Kenny B. Lipkowitz and Donald B. Boyd, Semi empirical Molecular Orbital Methods, J.J.P. Stewart, in Reviews in Computational Chemistry, VCH Publishers, 1990, 1, pages 45-81.
- Seydel, J.K., Prediction of in Vitro Activity of Sulfonamides, Using Hammett Constants or Spectrophotometric Data of the Basic Amines for Calculation. Mol. Pharmacol., 1966, 2: 259-265.
- So, Sung-Sau; Karplus Martin, Evolution Optimization in Quantitative Structure-Activity Relationship: An Application of Genetic Neural Networks J. Med. Chem. 1996, 39, 1521-1530
- Wayne C Guida, Software for structure-based drug design [Review article] Current Opinion in Structural Biology 1994, 4:777-781.
- L Mario Amzel, Structure-based drug design [Review article], Current Opinion in Biotechnology 1998, 9:366-369.
- T parun and CL Propst Computer Aided Drug Design Marcell Dekker, Inc. Network PP2-4 ,1989
- The complex of the anti-cancer therapeutic, BW1843U89, with thymidylate synthase at 2.0 Å resolution: implications for a new mode of inhibition. Thomas J Stout, Robert M Stroud, Structure 1996, 4:67-77.
- Richard B Silverman, The Organic Chemistry Of Drug Design and Drug Action, North Western University, Second edition, Academic press.
Sheikh Mohammad Azhar, R. B. Saudagar and S. J. Daharwal*
Institute of Pharmacy, Pt. Ravishankar Shukla University, Raipur. 492 010.
(C. G.) India.
*Corresponding author Mr. S.J. Daharwal
has nearly 15 years of research and teaching experience. He is a hard working
researcher . Mr . Daharwal did his masters degree from Dept. of Pharmacy, of
Nagpur University. He has over 12 publications to his credit published in international
and national journals. His research interest extends from analytical methods,
Drug synthesis and computer added drug designing. Presently, he is working as
a Lecturer at Institute of Pharmacy Pt. Ravishankar Shukla University, Raipur,
(C.G.) Email- email@example.com.
Fax no. 917712263773
Mr. R. B. Saudagar has nearly 10 years of research and teaching
experience. Mr Saudagar did his masters degree from Dept. of Pharmacy, of SGSITS
Indore. He has over 5 publications to his credit published in international
and national journals. His research interest extends from analytical methods,
Drug synthesis. Presently, he is working as a Lecturer at Institute of Pharmacy
Pt. Ravishankar Shukla University, Raipur, (C.G.)
Mr. Shekh Mohd Azhar is presently final year student of B.
Pharm. from Institute of Pharmacy, Pt. Ravishankar Shukla University, Raipur.
He has good academic record and he was the best student of the institute in