Drug Design Also Sometimes Referred To As Rational Drug Design Is The Inventive Process Of Finding New Medications Based On The Knowledge Of The Biological Target

Drug design also sometimes referred to as rational drug design is the inventive process of finding new medications based on the knowledge of the biological target.[1] The drug is most commonly a organic small molecule which activates or inhibits the function of a biomolecule such as a protein which in turn results in a therapeutic benefit to the patient. In the most basic sense, drug design involves design of small molecules that are complementary in shape and charge to the biomolecular target to which they interact and therefore will bind to it. Drug design frequently but not necessarily relies on computer modeling techniques.[2] This type of modeling often referred to as computer-aided drug design. The phrase '"drug design" is to some extent a misnomer. What is really meant by drug design is ligand design. Modeling techniques for prediction of binding affinity are reasonably successful. However there are many other properties such as bioavailability, metabolic half life, lack of side effects, etc. that first must be optimized before a ligand can becomes a safe and efficacious drug. These other characteristics are often difficult to optimize using rational drug design techniques Typically a drug target is a key molecule involved in a particular metabolic or signaling pathway that is specific to a disease condition or pathology, or to the infectivity or survival of a microbial pathogen. Some approaches attempt to inhibit the functioning of the pathway in the diseased state by causing a key molecule to stop functioning. Drugs may be designed that bind to the active region and inhibit this key molecule. Another approach may be to enhance the normal pathway by promoting specific molecules in the normal pathways that may have been affected in the diseased state. In addition, these drugs should also be designed in such a way as not to affect any other important "offtarget" molecules that may be similar in appearance to the target molecule since drug interactions with off-target molecules may lead to undesirable side effects. Sequence homology is often used to identify such risks. Most commonly, drugs are organic small molecules but protein based drugs (also known as biologics) are becoming increasing more common. In addition mRNA based gene silencing technologies may have therapeutic applications.

[edit] Types

Flow charts of two strategies of structure-based drug design There are two major types of drug design. The first is referred to as ligand-based drug design and the second, structure-based drug design.

[edit] Ligand based Ligand-based drug design (or indirect drug design) relies on knowledge of other molecules that bind to the biological target of interest. These other molecules may be used to derive a pharmacophore which defines the minimum necessary structural characteristics a molecule must possess in order to bind to the target.[3] In other words, a model of the biological target may be built based on the knowledge of what binds to it and this model in turn may be used to design new molecular entities that interact with the target.

[edit] Structure based Structure-based drug design (or direct drug design) relies on knowledge of the three dimensional structure of the biological target obtained through methods such as x-ray crystallography or NMR spectroscopy.[4] Using the structure of the biological target, candidate drugs that are predicted to bind with high affinity and selectivity to the target may be designed using interactive graphics and the intuition of a medicinal chemist. Alternatively various automated computational procedures may be used to suggest new drug candidates.

As the experimental methods as X-ray crystallography and NMR develop, the amount of information concerning 3D structures of biomolecular targets has increased dramatically, as well as the structural dynamic and electronic information about the ligands. This encourages the rapid development of the structure-based drug design. Current methods for structure-based drug design can be divided roughly into two categories. The first category is about “finding” ligands for a given receptor, which is usually referred as database searching. In this case, a large number of potential ligand molecules are screened to find those fitting the binding pocket of the receptor. This method is usually referred as ligand-based drug design. The key advantage of database searching is that it saves synthetic effort to obtain new lead compounds. Another category of structure-based drug design methods is about “building” ligands, which is usually referred as receptorbased drug design. In this case, ligand molecules are built up within the constraints of the binding pocket by assembling small pieces in a stepwise manner. These pieces can be either atoms or fragments. The key advantage of such a method is that novel structures, not contained in any database, can be suggested. These techniques are raising much excitement to the drug design community.[5][6][7] [edit] Active site identification Active site identification is the first step in this program. It analyzes the protein to find the binding pocket, derives key interaction sites within the binding pocket, and then prepares the necessary data for Ligand fragment link. The basic inputs for this step are the 3D structure of the protein and a pre-docked ligand in PDB format, as well as their atomic properties. Both ligand and protein atoms need to be classified and their atomic properties should be defined, basically, into four atomic types: • • • •

hydrophobic atom: all carbons in hydrocarbon chains or in aromatic groups. H-bond donor: Oxygen and nitrogen atoms bonded to hydrogen atom(s). H-bond acceptor: Oxygen and sp2 or sp hybridized nitrogen atoms with lone electron pair(s). Polar atom: Oxygen and nitrogen atoms that are neither H-bond donor nor Hbond acceptor, sulfur, phosphorus, halogen, metal and carbon atoms bonded to hetero-atom(s).

The space inside the ligand binding region would be studied with virtual probe atoms of the four types above so the chemical environment of all spots in the ligand binding region can be known. Hence we are clear what kind of chemical fragments can be put into their corresponding spots in the ligand binding region of the receptor. [edit] Ligand fragment link

Flow chart for structure based drug design When we want to plant “seeds” into different regions defined by the previous section, we need a fragments database to choose fragments from. The term “fragment” is used here to describe the building blocks used in the construction process. The rationale of this algorithm lies in the fact that organic structures can be decomposed into basic chemical fragments. Although the diversity of organic structures is infinite, the number of basic fragments is rather limited. Before the first fragment, i.e. the seed, is put into the binding pocket, and add other fragments one by one. we should think some problems. First, the possibility for the fragment combinations is huge. A small perturbation of the previous fragment conformation would cause great difference in the following construction process. At the same time, in order to find the lowest binding energy on the Potential energy surface (PES) between planted fragments and receptor pocket, the scoring function calculation would be done for every step of conformation change of the fragments derived from every type of possible fragments combination. Since this requires a large amount of computation, one may think using other possible strategies to let the program works more efficiently. When a ligand is inserted into the pocket site of a receptor, conformation favor for these groups on the ligand that can bind tightly with receptor should be taken priority. Therefore it allows us to put several seeds at the same time into the regions that have significant interactions with the seeds and adjust their favorite conformation first, and then connect those seeds into a continuous ligand in a manner that make the rest part

of the ligand having the lowest energy. The conformations of the pre-placed seeds ensuring the binding affinity decide the manner that ligand would be grown. This strategy reduces calculation burden for the fragment construction efficiently. On the other hand, it reduces the possibility of the combination of fragments, which reduces the number of possible ligands that can be derived from the program. These two strategies above are well used in most structure-based drug design programs. They are described as “Grow” and “Link”. The two strategies are always combined in order to make the construction result more reliable.[5][6][8] [edit] Scoring method Main article: Scoring functions for docking Structure-based drug design attempts to use the structure of proteins as a basis for designing new ligands by applying accepted principles of molecular recognition. The basic assumption underlying structure-based drug design is that a good ligand molecule should bind tightly to its target. Thus, one of the most important principles for designing or obtaining potential new ligands is to predict the binding affinity of a certain ligand to its target and use it as a criterion for selection.

A breakthrough work was done by Böhm[9] to develop a general-purposed empirical function in order to describe the binding energy. The concept of the “Master Equation” was raised. The basic idea is that the overall binding free energy can be decomposed into independent components which are known to be important for the binding process. Each component reflects a certain kind of free energy alteration during the binding process between a ligand and its target receptor. The Master Equation is the linear combination of these components. According to Gibbs free energy equation, the relation between dissociation equilibrium constant, Kd and the components of free energy alternation was built. The sub models of empirical functions differ due to the consideration of researchers. It has long been a scientific challenge to design the sub models. Depending on the modification of them, the empirical scoring function is improved and continuously consummated.[10][11][12]

[edit] Rational drug discovery In contrast to traditional methods of drug discovery which rely on trial-and-error testing of chemical substances on cultured cells or animals, and matching the apparent effects to

treatments, rational drug design begins with a hypothesis that modulation of a specific biological target may have therapeutic value. In order for a biomolecule to be selected as a drug target, two essential pieces of information are required. The first is evidence that modulation of the target will have therapeutic value. This knowledge may come from, for example, disease linkage studies that show an association between mutations in the biological target and certain disease states. The second is that the target is "drugable". This means that it is capable of binding to a small molecule and that its activity can be modulated by the small molecule. Once a suitable target has been identified, the target is normally cloned and expressed. The expressed target is then used to establish a screening assay. In addition, the threedimensional structure of the target may be determined. The search for small molecules that bind to the target is begun by screening libraries of potential drug compounds. This may be done by using the screening assay (a "wet screen"). In addition, if the structure of the target is available, a virtual screen may be performed of candidate drugs. Ideally the candidate drug compounds should be "druglike", that is they should possess properties that are predicted to lead to oral bioavailability, adequate chemical and metabolic stability, and minimal toxic effects. One way of estimating druglikeness is Lipinski's Rule of Five. Several methods for predicting drug metabolism have been proposed in the scientific literature, and a recent example is SPORCalc.[13] Due to the complexity of the drug design process, two terms of interest are still serendipity and bounded rationality. Those challenges are caused by the large chemical space describing potential new drugs without side-effects.

[edit] Computer-assisted drug design Computer-assisted drug design uses computational chemistry to discover, enhance, or study drugs and related biologically active molecules. The most fundamental goal is to predict whether a given molecule will bind to a target and if so how strongly. Molecular mechanics or molecular dynamics are most often used to predict the conformation of the small molecule and to model conformational changes in the biological target that may occur when the small molecule binds to it. Semi-empirical, ab initio quantum chemistry methods, or density functional theory are often used to provide optimized parameters for the molecular mechanics calculations and also provide an estimate of the electronic properties (electrostatic potential, polarizability, etc.) of the drug candidate which will influence binding affinity. Molecular mechanics methods may also be used to provide semi-quantitative prediction of the binding affinity. Alternatively knowledge based scoring function may be used to provide binding affinity estimates. These methods use linear regression, machine learning, neural nets or other statistical techniques to derive predictive binding affinity equations by fitting experimental affinities to computationally derived interaction energies between the small molecule and the target.

Ideally the computational method should be able to predict affinity before a compound is synthesized and hence in theory only one compound needs to be synthesized. The reality however is that present computational methods provide at best only qualitative accurate estimates of affinity. Therefore in practice it still takes several iterations of design, synthesis, and testing before an optimal molecule is discovered. On the other hand, computational methods have accelerated discovery by reducing the number of iterations required and in addition have often provided more novel small molecule structures. Drug design with the help of computers may be used at any of the following stages of drug discovery: 1. hit identification using virtual screening (structure- or ligand-based design) 2. hit-to-lead optimization of affinity and selectivity (structure-based design, QSAR, etc.) 3. lead optimization optimization of other pharmaceutical properties while maintaining affinity

Introduction 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 Early Years QSAR date back to the 19th century. In 1863, A.F.A. Cros at the University of Strasbourg observed that toxicity of alcohols to mammals increased as the water solubility of the alcohols decreased [1]. In the 1890's, Hans Horst Meyer of the University of Marburg and Charles Ernest Overton of the University of Zurich, working independently, noted that the toxicity of organic compounds depended on their lipophilicity [1,2]. Linear Free Energy Relationships Little additional development of QSAR occurred until the work of Louis Hammett (18941987), who correlated electronic properties of organic acids and bases with their equilibrium constants and reactivity. Consider the dissociation of benzoic acid:

Hammett observed that adding substituents to the aromatic ring of benzoic acid had an orderly and quantitative effect on the dissociation constant. For example,

a nitro group in the meta position increases the dissociation constant, because the nitro group is electron-withdrawing, thereby stabilizing the negative charge that develops. Consider now the effect of a nitro group in the para position:

The equilibrium constant is even larger than for the nitro group in the meta position, indicating even greater electron-withdrawal. Now consider the case in which an ethyl group is in the para position:

In this case, the dissociation constant is lower than for the unsubstituted compound, indicating that the ethyl group is electron-donating, thereby destabilizing the negative charge that arises upon dissociation. Hammett also observed that substituents have a similar effect on the dissociation of other organic acids and bases. Consider the dissociation of phenylacetic acids:

Electron-withdrawal by the nitro group increases dissociation, with the effect being less for the meta than for the para substituent, just as was observed with benzoic acid. The electron-donating ethyl group decreases the equilibrium constant, as would be expected. Data for these equilibria typically are graphed as illustrated below:

Figure 1: Example of a graph for a linear free energy relationship. K0 or K0' represent equilibrium constants for unsubstituted compounds and K or K', for substituted compounds. Values for the abscissa are calculated from the dissociation constants of unsubstituted and substituted benzoic acid. Values for the ordinate are obtained from another organic acid or base with identical patterns of substitution, in this case phenylacetic acid. Because this relationship is linear, the following equation can be written:

where is the slope of the line. The values for the abscissa in Figure 1 are always those for benzoic acid and are given the symbol, . Therefore, we can write:

, the slope of the line, is a proportionality constant pertaining to a given equilibrium. It relates the effect of substituents on that equilibrium to the effect of those substituents on the benzoic acid equilibrium. That is, if the effect of substituents is proportionally greater than on the benzoic acid equilibrium, then > 1; if the effect is less than on the benzoic acid equilibrium, < 1. By definition, for benzoic acid is equal to 1. is a descriptor of the substituents. The magnitude of gives the relative strength of the electron-withdrawing or -donating properties of the substituents. is positive if the substituent is electron-withdrawing and negative if it is electron-donating.

These relationships as developed by Hammett are termed linear free energy relationships. Recall the equation relating free energy to an equilibrium constant:

That is, the free energy is proportional to the logarithm of the equilibrium constant. These linear free energy relationships are termed "extrathermodynamic". Although they can be stated in terms of thermodynamic parameters, no thermodynamic principle states that the relationships should be true. To develop a better understanding of these relationships, it is instructive to consider some values of and . Values of are provided below:

In the aniline and phenol equilibria, the hydrogen ion that is dissociating is one atom removed from the phenyl ring, whereas in the benzoic acid equilibrium it is two atoms removed. Thus, substituents are able to exert a greater effect on the dissociation in aniline and phenol than in benzoic acid and the value of > 1. In phenylacetic and phenylpropionic acids, the hydrogen ion dissociating is three and four atoms removed, respectively, from the phenyl ring. Substituents are able to exert a lesser effect on the equilibrium than on the benzoic acid equilibrium and < 1. Some illustrative values of for substituents in the meta and para positions are given below:

By definition, for hydrogen is 0. The positive values of for the nitro group indicate that it is electron-withdrawing. In understanding the magnitudes of the values for the nitro group in meta vs. para positions, consider the mechanisms of electron withdrawal or

donation. For a nitro group in the meta position, electron-withdrawal is due to an inductive effect produced by the electronegativity of the constituent atoms. If only induction were operative, one would expect the electron-withdrawing effect of a nitro group in the para position to be less than in the meta position. The larger value for a parasubstituted nitro group results from the combination of both inductive and resonance effects. For chlorine, the electronegativity of the atom produces an inductive electronwithdrawing effect, with the magnitude of the effect in the para position being less than in the meta position. For chlorine, only the inductive effect is possible. The methoxy group can be electron-donating or -withdrawing, depending on the position of substitution. In the meta position, the electronegativity of the oxygen produces an inductive electron-withdrawing effect. In the para position, only a small inductive effect would be expected. Moreover, an electron-donating resonance effect occurs for the methoxy group in the para position, giving an overall electron-donating effect. Tables of values for numerous substituents have been published [3,4]. In some cases, the sigma values are generally applicable to many different equilibria. In other cases, sigma values have been derived for specific equilibria, which is particularly true when one considers sigma values for ortho substituents.

Applications of the Hammett Equation Illustrative examples of the application of the Hammett relationship will be presented. The first is the prediction of the pKa of ionization equilibria. Recall the relationship

Therefore,

which for benzoic acid is

Consider the substituted benzoic acid

Given = 0.71 for nitro groups and = -0.13 for methyl groups, we calculate pKa=2.91, which compares favorably with the experimental value of 2.97. The second example illustrates the applicability of Hammett's electronic descriptors in a QSAR relating the inhibition of bacterial growth by a series of sulfonamides,

where X represents various substituents [5,6]. A QSAR was developed based on the values of the substituents,

where C is the minimum concentration of compound that inhibited growth of E. coli. From this relationship, we see that electron-withdrawing substituents favor inhibition of growth.

Hansch Analysis QSAR based on Hammett's relationship utilize electronic properties as the descriptors of structures. Difficulties were encountered when investigators attempted to apply Hammett-type relationships to biological systems, indicating that other structural descriptors were necessary. Robert Muir, a botanist at Pomona College, was studying the biological activity of compounds that resembled indoleacetic acid and phenoxyacetic acid, which function as plant growth regulators. In attempting to correlate the structures of the compounds with their activities, he consulted his colleague in chemistry, Corwin Hansch. Using Hammett sigma parameters to account for the electronic effect of substituents did not lead to meaningful QSAR. However, Hansch recognized the importance of the lipophilicity, expressed as the octanol-water partition coefficient, on biological activity [7]. We now recognize this parameter to provide a measure of the bioavailability of compounds, which will determine, in part, the amount of the compound that gets to the target site. Relationships were developed to correlate a structural parameter (i.e., lipophilicity) with activity. In some cases, a univariate relationship correlating structure and activity was adequate. The form of the equation is:

where C is the molar concentration of compound that produces a standard response (e.g., LD50, ED50). With other data, it was observed that correlations were improved by combining Hammett's electronic parameters and Hansch's measure of lipophilicity using an equation such as

where is the Hammett substituent parameter and pi is defined analogously to . That is,

In yet other cases, parabolic relationships between biological response and hydrophobicity were observed that could be fit by including a (log P)**2 term in the QSAR. One interpretation to account for this term is that many membranes must be traversed for compounds to get to the target site, and those with greatest hydrophobicity will become localized in the membranes they encounter initially. Thus, an optimum hydrophobicity may be found in some test systems. QSAR are now developed using a variety of parameters as descriptors of the structural properties of molecules. Hammett sigma values are often used for electronic parameters, but quantum mechanically derived electronic parameters also may be used. Other descriptors to account for the shape, size, lipophilicity, polarizability, and other structural properties also have been devised. A QSAR database has been established at Pomona College that summarizes over 6000 datasets of biological and chemical QSAR.

Drug Design Researchers have attempted for many years to develop drugs based on QSAR. Easy access to computational resources was not available when these efforts began, so attempts consisted primarily of statistical correlations of structural descriptors with biological activities. However, as access to high-speed computers and graphics workstations became commonplace, this field has evolved into what is often termed rational drug design or computer-assisted drug design. We will discuss the application of QSAR to drug design, some examples of which relied primarily on statistical correlation and some, on computer-based visualization and modeling. An early example of QSAR in drug design involves a series of 1-(X-phenyl)3,3-dialkyl triazenes.

These compounds were of interest for their anti-tumor activity, but they also were mutagenic. QSAR was applied to understand how the structure might be modified to reduce the mutagenicity without significantly decreasing the anti-tumor activity. Mutagenic activity was evaluated in the Ames test, and from those data, the following QSAR was developed:

where C is the molar concentration required to give 30 revertants per 10**8 bacteria and is a "through resonance" electronic parameter [8,9]. From the equation, it is seen that factors that favor mutagenicity are increased lipophilicity and electron-donating substituents. Studies of the anti-tumor activity were done against L1210 leukemia in mice. From the data, the following QSAR was developed:

where C is the molar concentration of compound producing a 40% increase in life span of mice, MR is molar refractivity, which is a measure of molecular volume, and EsR is a steric parameter for the R group [10]. Based on these equations, mutagenicity is more sensitive than anti-tumor activity to the electronic effects of the substituents. Thus, electron-withdrawing substituents were examined, as illustrated in the example below:

By substituting a sulfonamide group at the para position, the anti-tumor activity was reduced 1.2-fold, whereas the mutagenicity was reduced by about 400-fold.

Computer-Assisted Design

Computer-assisted drug design (CADD), also called computer-assisted molecular design (CAMD), represents more recent applications of computers as tools in the drug design process. In considering this topic, it is important to emphasize that computers cannot substitute for a clear understanding of the system being studied. That is, a computer is only an additional tool to gain better insight into the chemistry and biology of the problem at hand. In most current applications of CADD, attempts are made to find a ligand (the putative drug) that will interact favorably with a receptor that represents the target site. Binding of ligand to the receptor may include hydrophobic, electrostatic, and hydrogen-bonding interactions. In addition, solvation energies of the ligand and receptor site also are important because partial to complete desolvation must occur prior to binding. This approach to CADD optimizes the fit of a ligand in a receptor site. However, optimum fit in a target site does not guarantee that the desired activity of the drug will be enhanced or that undesired side effects will be diminished. Moreover, this approach does not consider the pharmacokinetics of the drug. The approach used in CADD is dependent upon the amount of information that is available about the ligand and receptor. Ideally, one would have 3-dimensional structural information for the receptor and the ligand-receptor complex from X-ray diffraction or NMR. The ideal is seldom realized. In the opposite extreme, one may have no experimental data to assist in building models of the ligand and receptor, in which case computational methods must be applied without the constraints that the experimental data would provide. Based on the information that is available, one can apply either ligand-based or receptorbased 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. 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. This template may then be used to develop new compounds with functional groups in the desired positions. In applying this strategy, one must recognize that one is assuming that it is the minimum energy conformers that will bind most favorably in the receptor site. In fact, there is no a priori reason to exclude higher energy conformers as the source of activity.

The receptor-based approach to CADD applies when a reliable model of the receptor site is available, as from X-ray diffraction, NMR, or homology modeling. With the availability of the receptor site, the problem is to design ligands that will interact favorably at the site, which is a docking problem. An Example of CADD: Carbonic anhydrase Carbonic anhydrase catalyzes the reaction ,the hydration of some aldehydes and ketones, and the hydrolysis of alkyl and aryl esters. It is a zinccontaining enzyme of about 30,000 daltons, and the three-dimensional structure has been characterized by X-ray diffraction. Physiologically, carbonic anhydrase is involved in gastric, urinary, pancreatic, lacrimal, and cerebrospinal secretions. Inhibitors of carbonic anhydrase include aromatic and heterocyclic sulfonamides, and some of these compounds have found application as diuretics. Both traditional QSAR and computer graphical methods have been applied to the development of sulfonamides and other compounds as inhibitors of carbonic anhydrase. For example, Hansch et al. [11] developed a QSAR based on the binding constants of 29 phenylsulfonamides to the enzyme. The equation that was derived was the following:

where K is the binding constant, I1=1 if X is meta and 0 otherwise, and, I2 = 1 if X is ortho and 0 otherwise. The negative coefficients of I1 and I2 suggest that they account for unfavorable steric effects when substituents are in the meta or ortho positions. Binding is favored by electron-withdrawing substituents, which is consistent with the hypothesis that the ionized form of -SO2NH2 binds to the zinc in the active site of carbonic anhydrase [12]. Interactive computer graphics also was applied to understand better the interaction of carbonic anhydrase inhibitors with the enzyme as illustrated in Figure 2 [11].

Figure 2: Active site of carbonic anhydrase containing the inhibitor MTS [(4S-trans)-4(methylamino)-5,6-dihydro-6-methyl-4H-thieno (2,3-B)thiopyran-2-sulfonamide-7,7dioxide]. The image was prepared from the PDB file 1cin.pdb. The active site is a cavity approximately 12 Angstroms deep with a zinc atom (magenta) near the bottom of the cavity. The active site is divided into a hydrophilic half (blue) and a hydrophobic half (red). In the complex, the inhibitor appears to be bound such that the sulfonamide moiety occupies the fourth coordination site of the zinc atom, with the other three sites being occupied by histidine residues. For subsequent discussion, note that the active site is much larger than is required to accommodate an inhibitor of this size. Receptor-based drug design incorporates a number of molecular modeling techniques, one of which is docking. The Kuntz research group [13] applied their DOCK program to the identification of compounds that may inhibit carbonic anhydrase. Structures of two of the candidates are shown below.

These molecules are considerably larger than the arylsulfonamides that traditionally are used as carbonic anhydrase inhibitors. In fact, no arylsulfonamides were identified as potential inhibitors in this study. These results probably arise because scoring of candidates was based on the size and shape of the molecules. These large candidates can engage in a greater number of favorable interactions within the large carbonic anhydrase active site than can the smaller arylsulfonamides. More recent versions of DOCK allow scoring based on force fields, which include both van der Waals and electrostatic interactions [14]. These results with DOCK illustrate the potential for programs such as this one to search objectively for ligands than are complementary to receptor sites, thereby assisting researchers in identifying potential drugs than may be considerably different from existing drugs. As yet, the efficacy as drugs of these candidates identified by DOCK has not been demonstrated.

Applications of Other Modeling Techniques Once potential drugs have been identified by the methods described above, other molecular modeling techniques may then be applied. For example, geometry optimization may be used to "relax" the structures and to identify low energy orientations of drugs in receptor sites. Molecular dynamics may assist in exploring the energy landscape, and free energy simulations can be used to compute the relative binding free energies of a series of putative drugs.