Three-dimensional quantitative structure-activity relationship of angiotesin-converting enzyme and thermolysin inhibitors. II. A comparison of CoMFA models incorporating molecular orbital fields and desolvation free energies based on active-analog and complementary-receptor-field alignment rules

Role of a remote leucine residue in the catalytic function of polyol dehydrogenase

This study examined the role of remote residues on the structure and function of zinc-dependent polyol dehydrogenases.

The continuous molecular fields approach to building 3D-QSAR models

The continuous molecular fields (CMF) approach is based on the application of continuous functions for the description of molecular fields instead of finite sets of molecular descriptors (such as interaction energies computed at grid nodes) commonly used for this purpose. These functions can be encapsulated into kernels and combined with kernel-based machine learning algorithms to provide a variety of novel methods for building classification and regression structure-activity models, visualizing chemical datasets and conducting virtual screening. In this article, the CMF approach is applied to building 3D-QSAR models for 8 datasets through the use of five types of molecular fields (the electrostatic, steric, hydrophobic, hydrogen-bond acceptor and donor ones), the linear convolution molecular kernel with the contribution of each atom approximated with a single isotropic Gaussian function, and the kernel ridge regression data analysis technique. It is shown that the CMF approach even in this simplest form provides either comparable or enhanced predictive performance in comparison with state-of-the-art 3D-QSAR methods.

Specificity of binding with matrix metalloproteinases

Matrix metalloproteinases (MMPs) regulate a wide range of biological functions; hence, they have invited great attention for the studies on their structures and functions, and since their overactivation leads to several diseases, the design and discovery of their potent inhibitors have become the need of the day. Since there have been so far discovered 28 different types of human MMPs, the specificity of binding of inhibitors with each different MMP needs special attention. The chapter presents the X-ray crystallographic and NMR studies on three-dimensional structures of a number of MMPs to reveal their catalytic site, subsites, specificity of binding with substrate and inhibitors, and catalytic mechanism. In addition to catalytic site, MMPs possess some subsites designated by unprimed and primed S, e.g., S1, S2, S3 and S1', S2', S3'. Among these, the S1' pocket varies the most among the different MMPs varying in both the amino acid makeup and depth of the pocket (shallow, intermediate, and deep pocket MMPs). This, along with the flexibility in the structures of MMPs, could be of great help in the design and the development of selective MMP inhibitors (MMPIs). The determination of affinity of inhibitors and the cleavage position of peptide substrates is mainly based on P1'-S1' interaction (P1', the group in inhibitor or substrate binding to S1' pocket of the enzyme), and it is the main determinant for the affinity of inhibitors and the cleavage position of peptide substrates.

3D QSAR pharmacophore modeling, in silico screening, and density functional theory (DFT) approaches for identification of human chymase inhibitors

Human chymase is a very important target for the treatment of cardiovascular diseases. Using a series of theoretical methods like pharmacophore modeling, database screening, molecular docking and Density Functional Theory (DFT) calculations, an investigation for identification of novel chymase inhibitors, and to specify the key factors crucial for the binding and interaction between chymase and inhibitors is performed. A highly correlating (r = 0.942) pharmacophore model (Hypo1) with two hydrogen bond acceptors, and three hydrophobic aromatic features is generated. After successfully validating "Hypo1", it is further applied in database screening. Hit compounds are subjected to various drug-like filtrations and molecular docking studies. Finally, three structurally diverse compounds with high GOLD fitness scores and interactions with key active site amino acids are identified as potent chymase hits. Moreover, DFT study is performed which confirms very clear trends between electronic properties and inhibitory activity (IC(50)) data thus successfully validating "Hypo1" by DFT method. Therefore, this research exertion can be helpful in the development of new potent hits for chymase. In addition, the combinational use of docking, orbital energies and molecular electrostatic potential analysis is also demonstrated as a good endeavor to gain an insight into the interaction between chymase and inhibitors.

A comparison of different electrostatic potentials on prediction accuracy in CoMFA and CoMSIA studies

Computational chemistry is playing an increasingly important role in drug design and discovery, structural biology, and quantitative structure-activity relationship (QSAR) studies. For QSAR work, selecting an appropriate and accurate method to assign the electrostatic potentials of each atom in a molecule is a critical first step. So far several commonly used methods are available to assign charges. However, no systematic comparison of the effects of electrostatic potentials on QSAR quality has been made. In this study, twelve semi-empirical and empirical charge-assigning methods, AM1, AM1-BCC, CFF, Del-Re, Formal, Gasteiger, Gasteiger-Hückel, Hückel, MMFF, PRODRG, Pullman, and VC2003 charges, have been compared for their performances in CoMFA and CoMSIA modeling using several standard datasets. Some charge assignment models, such as Del-Re, PRODRG, and Pullman, are limited to specific atom and bond types, and, therefore, were excluded from this study. Among the remaining nine methods, the Gasteiger-Hückel charge, though commonly used, performed poorly in prediction accuracy. The AM1-BCC method was better than most charge-assigning methods based on prediction accuracy, though it was not successful in yielding overall higher cross-validation correlation coefficient (q(2)) values than others. The CFF charge model worked the best in prediction accuracy when q(2) was used as the evaluation criterion. The results presented should help the selection of electrostatic potential models in CoMFA and CoMSIA studies.

Partial charge calculation method affects CoMFA QSAR prediction accuracy

The 3D-QSAR method comparative molecular field analysis (CoMFA) involves the estimation of atomic partial charges as part of the process of calculating molecular electrostatic fields. Using 30 data sets from the literature the effect of using different common partial charge calculation methods on the predictivity (cross-validated R2) of CoMFA was studied. The partial charge methods ranged from the popular Gasteiger and the newer MMFF94 electronegativity equalization methods, to the more complex and computationally expensive semiempirical charges AM1, MNDO, and PM3. The MMFF94 and semiempirical MNDO, AM1, and PM3 methods for computing charges were found to result in statistically significantly more predictive CoMFA models than the Gasteiger charges. Although there was a trend toward the semiempirical charges performing better than the MMFF94 charges, the difference was not statistically significant. Thus, semiempirical partial charge calculation methods are suggested for the most predictive CoMFA models, but the MMFF94 charge calculation method is a very good alternative if semiempirical methods are not available or faster calculation speed is important.

Fragment-based quantitative structure-activity relationship (FB-QSAR) for fragment-based drug design

In cooperation with the fragment-based design a new drug design method, the so-called "fragment-based quantitative structure-activity relationship" (FB-QSAR) is proposed. The essence of the new method is that the molecular framework in a family of drug candidates are divided into several fragments according to their substitutes being investigated. The bioactivities of molecules are correlated with the physicochemical properties of the molecular fragments through two sets of coefficients in the linear free energy equations. One coefficient set is for the physicochemical properties and the other for the weight factors of the molecular fragments. Meanwhile, an iterative double least square (IDLS) technique is developed to solve the two sets of coefficients in a training data set alternately and iteratively. The IDLS technique is a feedback procedure with machine learning ability. The standard Two-dimensional quantitative structure-activity relationship (2D-QSAR) is a special case, in the FB-QSAR, when the whole molecule is treated as one entity. The FB-QSAR approach can remarkably enhance the predictive power and provide more structural insights into rational drug design. As an example, the FB-QSAR is applied to build a predictive model of neuraminidase inhibitors for drug development against H5N1 influenza virus.

Efficient calculation of molecular properties from simulation using kernel molecular dynamics

Understanding the relationship between chemical structure and function is a ubiquitous problem within the fields of chemistry and biology. Simulation approaches attack the problem utilizing physics to understand a given process at the particle level. Unfortunately, these approaches are often too expensive for many problems of interest. Informatics approaches attack the problem with empirical analysis of descriptions of chemical structure. The issue in these methods is how to describe molecules in a manner that facilitates accurate and general calculation of molecular properties. Here, we present a novel approach that utilizes aspects of simulation and informatics in order to formulate structure-property relationships. We show how supervised learning can be utilized to overcome the sampling problem in simulation approaches. Likewise, we show how learning can be achieved based on molecular descriptions that are rooted in the physics of dynamic intermolecular forces. We apply the approach to three problems including the analysis of corticosteroid binding globulin ligand binding affinity, identification of formylpeptide receptor ligands, and identification of resveratrol analogues capable of inhibiting activation of transcription factor nuclear factor kappaB.

Multiple field three dimensional quantitative structure–activity relationship (MF-3D-QSAR)

A new drug design method, the multiple field three-dimensional quantitative structure-activity relationship (MF-3D-QSAR), is proposed. It is a combination and development of classical 2D-QSAR and traditional 3D-QSAR. In addition to the electrostatic and van der Waals potentials, more potential fields (such as lipophilic potential, hydrogen bonding potential, and nonthermodynamic factors) are integrated in the MF-3D-QSAR. Meanwhile, a principal component analysis (PCA) and iterative double least square (IDLS) technique is developed for predicting the bioactivity of query drug candidates. As an example, the MF-3D-QSAR is applied to the design of neuraminidase inhibitor and to prove its predictive power, and some useful findings are obtained for developing drugs against influenza virus.

Comparative residue interaction analysis (CoRIA): a 3D-QSAR approach to explore the binding contributions of active site residues with ligands

A novel approach termed comparative residue-interaction analysis (CoRIA), emphasizing the trends and principles of QSAR in a ligand-receptor environment has been developed to analyze and predict the binding affinity of enzyme inhibitors. To test this new approach, a training set of 36 COX-2 inhibitors belonging to nine families was selected. The putative binding (bioactive) conformations of inhibitors in the COX-2 active site were searched using the program DOCK. The docked configurations were further refined by a combination of Monte Carlo and simulated annealing methods with the Affinity program. The non-bonded interaction energies of the inhibitors with the individual amino acid residues in the active site were then computed. These interaction energies, plus specific terms describing the thermodynamics of ligand-enzyme binding, were correlated to the biological activity with G/PLS. The various QSAR models obtained were validated internally by cross validation and boot strapping, and externally using a test set of 13 molecules. The QSAR models developed on the CoRIA formalism were robust with good r (2), q (2) and r (pred) (2) values. The major highlights of the method are: adaptation of the QSAR formalism in a receptor setting to answer both the type (qualitative) and the extent (quantitative) of ligand-receptor binding, and use of descriptors that account for the complete thermodynamics of the ligand-receptor binding. The CoRIA approach can be used to identify crucial interactions of inhibitors with the enzyme at the residue level, which can be gainfully exploited in optimizing the inhibitory activity of ligands. Furthermore, it can be used with advantage to guide point mutation studies. As regards the COX-2 dataset, the CoRIA approach shows that improving Coulombic interaction with Pro528 and reducing van der Waals interaction with Tyr385 will improve the binding affinity of inhibitors.

Synthesis and SAR/3D-QSAR studies on the COX-2 inhibitory activity of 1,5-diarylpyrazoles to validate the modified pharmacophore

Diverse analogs of 1,5-diarylpyrazoles having 3-hydroxymethyl-4-sulfamoyl (SO2NH2)/methyl sulfonyl (SO2Me)-pheny group at N1 were synthesized and evaluated for their in vitro cyclooxygenase (COX-1/COX-2) inhibitory activity. The SAR study mainly involved the variations at positions C-3, C-5 and N1 of the pyrazole ring. Several small hydrophobic groups at/around position-4 of C-5 phenyl, viz. 3,4-dimethylphenyl analog 9, 3-methyl-4-methylsulfanylphenyl analog 14 and 2,3-dihydrobenzo[b]thiophenyl analog 17, exhibited impressive COX-2 inhibitory potency. In general, the sulfonamide analogues with a CHF2 at C-3 were found to be more potent than those having a CF3 group. The three dimensional quantitative structure activity relationship comprising comparative molecular field analysis (3D-QSAR-CoMFA) afforded the models with high predictivity which further validated the acceptance of hydroxymethyl (CH2OH) group in the hydrophilic pocket of the COX-2 enzyme.

3D-QSAR comparative molecular field analysis on delta opioid receptor agonist SNC80 and its analogs

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were constructed using comparative molecular field analysis (CoMFA) for a series of delta opioid receptor agonists: SNC80 analogs. Quantum chemical calculations on SNC80 show that protonation is preferred at the basic N4 atom over the alternative N1 atom, accordingly N4 protonation may contribute significantly to ligand-receptor interactions under physiologically relevant conditions. Statistically significant and predictive CoMFA models were achieved by pooling biological data from independent published sources, including compounds with both alphaR and alphaS benzylic configurations. Improved CoMFA models were obtained when the compounds were considered as N4-protonated species rather than neutral compounds. The influence of various atomic partial-charge formalisms, alignment schemes and additional molecular descriptors was evaluated in order to produce the highest quality models. In addition, separate CoMFA models were generated for compounds with only the alphaR benzylic configuration. These CoMFA models showed excellent internal predictability and consistency, and external validation using test-set compounds yielded predicted pIC50 values within 1log unit of the corresponding experimentally measured values. Key insights into the structure-activity relationship derived from the CoMFA analysis concur with experimentally observed data, thus the CoMFA models presented here find utility for predicting the binding affinity, and guiding the design, of novel SNC80 analogs and related delta opioid receptor agonists.

Domain-Selective Ligand-Binding Modes and Atomic Level Pharmacophore Refinement in Angiotensin I Converting Enzyme (ACE) Inhibitors

Somatic ACE (EC 3.4.15.1), a Zn(II) metalloproteinase, is composed of functionally active N and C domains resulting from tandem gene duplication. Despite the high degree of sequence similarity between the two domains, they differ in substrate and inhibitor specificity and in their activation by chloride ions. Because of the critical role of ACE in cardiovascular and renal diseases, both domains are attractive targets for drug design. Putative structural models have been generated for the interactions of ACE inhibitors (lisinopril, captoril, enalaprilat, keto-ACE, ramiprilat, quinaprilat, peridoprilat, fosinoprilat, and RXP 407) with both the ACE_C and the ACE_N domains. Inhibitor-domain selectivity was interpreted in terms of residue alterations observed in the four subsites of the binding grooves of the ACE_C/ACE_N domains (S1: V516/N494, V518/T496, S2: F391/Y369, E403/R381, S1': D377/Q355, E162/D140, V379/S357, V380/T358, and S2': D463/E431, T282/S260). The interactions governing the ligand-receptor recognition process in the ACE_C domain are: a salt bridge between D377, E162, and the NH(2) group (P1' position), a hydrogen bond of the inhibitor with Q281, the presence of bulky hydrophobic groups in the P1 and P2' sites, and a stacking interaction of F391 with a benzyl group in the P2 position. In ACE_N these interactions are: hydrogen bonds of the inhibitor with E431, Y369, and R381, and a salt bridge between the carboxy group in the P2 position of the inhibitor and R500. The calculated complexes were evaluated for their consistency with structure-activity relationships and site-directed mutagenesis data. A comparison between the calculated interaction free energies and the experimentally observed biological activities was also made. Pharmacophore refinement was achieved at an atomic level, and might provide an improved basis for structure-based rational design of second-generation, domain-selective inhibitors.

Computer-aided design of non sulphonyl COX-2 inhibitors: an improved comparative molecular field analysis incorporating additional descriptors and comparative molecular similarity indices analysis of 1,3-diarylisoindole derivatives

A set of thirty five molecules of 1,3-diaryl-4,5,6,7-tetrahydro-2H-isoindoles endowed with selective COX-2 inhibitory activity was analyzed using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Besides conventional steric and electrostatic fields, seven additional descriptors were incorporated to the CoMFA models. An improved CoMFA model (r(2)(cv)=0.536, r(2)(conv)=0.968, SEE=0.222, r(2)(pred)=0.6564) was obtained by taking into account the CMR as additional descriptor. This analysis provided useful information regarding the pharmacophoric requirements for COX-2 inhibitory activity. FlexX was used to find out the binding orientation of this new class of 1,3-diaryl isoindoles in the active site of COX-2. The contour maps produced by improved CoMFA model was superimposed onto the active site revealing a good correlation between the contour maps and the active site residue interactions.

Deglycosylation, processing and crystallization of human testis angiotensin-converting enzyme

Angiotensin I-converting enzyme (ACE) is a highly glycosylated type I integral membrane protein. A series of underglycosylated testicular ACE (tACE) glycoforms, lacking between one and five N-linked glycosylation sites, were used to assess the role of glycosylation in tACE processing, crystallization and enzyme activity. Whereas underglycosylated glycoforms showed differences in expression and processing, their kinetic parameters were similar to that of native tACE. N-glycosylation of Asn-72 or Asn-109 was necessary and sufficient for the production of enzymically active tACE but glycosylation of Asn-90 alone resulted in rapid intracellular degradation. All mutants showed similar levels of phorbol ester stimulation and were solubilized at the same juxtamembrane cleavage site as the native enzyme. Two mutants, tACEDelta36-g1234 and -g13, were successfully crystallized, diffracting to 2.8 and 3.0 A resolution respectively. Furthermore, a truncated, soluble tACE (tACEDelta36NJ), expressed in the presence of the glucosidase-I inhibitor N -butyldeoxynojirimycin, retained the activity of the native enzyme and yielded crystals belonging to the orthorhombic P2(1)2(1)2(1) space group (cell dimensions, a=56.47 A, b=84.90 A, c=133.99 A, alpha=90 degrees, beta=90 degrees and gamma=90 degrees ). These crystals diffracted to 2.0 A resolution. Thus underglycosylated human tACE mutants, lacking O-linked oligosaccharides and most N-linked oligosaccharides or with only simple N-linked oligosaccharides attached throughout the molecule, are suitable for X-ray diffraction studies.

Three-dimensional quantitative structure-activity relationship for several bioactive peptides searched by a convex hull-comparative molecular field analysis approach

Three-dimensional (3D) convex hulls are computed for theoretically generated structures of a group of 18 bioactive tachykinin peptides. The number of peptides treated as a training set is 14, whereas that treated as a test set is four. The frequency of atoms of the same atomic type lying at the vertices of all the hulls computed for all the structures in a structural set is counted. Vertex atoms with non-zero frequency counted are collected together as a set of commonly exposed groups. These commonly exposed atoms are then treated as a set of correspondences for aligning all the other 13 structures in a structural set against a common template, which is the structure of the most potent peptide in the set using the FIT module of the SYBYL 6.6 program. Each aligned structural set is then analyzed by the comparative molecular field analysis (CoMFA) module using the C.3 probe having a charge of +1.0. The corresponding cross-validated r2 values range from -0.99 to 0.57 for a number of 73 structural sets studied. The comparative molecular similarity indices analysis (CoMSIA) module within the SYBYL 6.6 package is also used to analyze some of these aligned structural sets. Although the CoMSIA results are in accord with those of CoMFA, it is also found that the CoMFA results of several structural sets can be improved somewhat for conformations of the structures in the sets that are adjusted by constraint energy minimization and then constraint molecular dynamics simulation runs using distance constraints derived from some commonly exposed groups determined for them. This result further implies that the convex hull-CoMFA is a feasible approach to screen the bioactive conformations for molecules of this class.

Novel variable selection quantitative structure--property relationship approach based on the k-nearest-neighbor principle

A novel automated variable selection quantitative structure-activity relationship (QSAR) method, based on the kappa-nearest neighbor principle (kNN-QSAR) has been developed. The kNN-QSAR method explores formally the active analogue approach, which implies that similar compounds display similar profiles of pharmacological activities. The activity of each compound is predicted as the average activity of K most chemically similar compounds from the data set. The robustness of a QSAR model is characterized by the value of cross-validated R2 (q2) using the leave-one-out cross-validation method. The chemical structures are characterized by multiple topological descriptors such as molecular connectivity indices or atom pairs. The chemical similarity is evaluated by Euclidean distances between compounds in multidimensional descriptor space, and the optimal subset of descriptors is selected using simulated annealing as a stochastic optimization algorithm. The application of the kNN-QSAR method to 58 estrogen receptor ligands as well as to several other groups of pharmacologically active compounds yielded QSAR models with q2 values of 0.6 or higher. Due to its relative simplicity, high degree of automation, nonlinear nature, and computational efficiency, this method could be applied routinely to a large variety of experimental data sets.

A comparative molecular field analysis study on several bioactive peptides using the alignment rules derived from identification of commonly exposed groups

A 3D convex hull computation algorithm designed by us previously is used as a tool to align a series of structures randomly generated for eleven bioactive tachykinin peptides. There are 10 random structures generated for each peptide. A random structure is selected for each peptide to form a structural set of eleven structures. The total number of structural sets generated is 100. The convex hull computation algorithm is applied to each peptide structure generated. We count the frequency of atoms lying on the vertices of each hull computed. Vertices of the same atom type are gathered together as a set of commonly exposed atoms for a structural set generated. Structures are then aligned by treating the set of commonly exposed atoms as a set of correspondences using the FIT option of the SYBYL 6.4 program. All the structure sets are also aligned by using the coordinates of the backbone C alpha atoms as a set of correspondences through the same program. It is found that while a smaller degree of structural similarity for structures aligned by the convex hull alignment rule is detected, the overall SYBYL comparative molecular field analysis (CoMFA) statistics computed for the aligned structures using the alignment rule is better than that computed for the aligned structures using the C alpha atoms alignment rule. A similar conclusion is drawn for a subset of structures selected and probed by a different type of atoms using the SYBYL CoMFA program. These results indicate that computation of 3D convex hulls is a feasible way that one can use to align structures generated for highly flexible molecules of this kind.

Quantitative structure-activity relationships of antihypertensive agents

Quantitative structure-activity relationships of various classes of antihypertensive agents, e.g., sympatholytic agents, diuretics, direct or peripheral vasodilators, potassium channel activators, angiotensin-converting enzyme inhibitors, renin inhibitors and miscellaneous agents (platelet aggregation inhibitors) are reviewed. This review gives an overall picture of the mode of action of each class of drugs and points out their specific physicochemical and structural properties governing their action. For example, in the case of centrally acting drugs (sympatholytic agents) it has surfaced that the prime factors governing their activity are lipophilic and steric properties of the molecules, and at the receptor level a charge-transfer complex is formed between the molecule and the receptor. It is, however, observed that for peripherally acting sympatholytic agents the prime role is played by only lipophilicity. In the case of diuretics, the electronic characters of molecules are found to be more dominant than their lipophilic property, but for direct vasodilators and ACE inhibitors both electronic and lipophilic properties seem to be equally important. In renin or platelet aggregation inhibitors, the structural properties appear to be more important. However, the fundamental property that is overwhelmingly involved in the majority of antihypertensive agents appears to be the lipophilicity, suggesting that in most of the cases the hydrophobic interaction would play the major role in drug action.

Alignment of flexible molecules at their receptor site using 3D descriptors and Hi-PCA

Three categories of molecular flexibility are defined. A novel method of aligning partly flexible molecules with each other is described. The binding mode of one of these molecules to its receptor site was already well known from previous crystallographic studies, and this known binding mode was used to predict the binding mode of the other molecules at their receptor. The predictions were checked by comparison with previous observations, and were correct. Two novel methods were combined in this research. It was necessary to take account of the conformational changes which occur when each ligand molecule binds to the protein, and a new release of programme Grid was used for this. It was also necessary to analyse the Grid results in order to distinguish the role of each chemical group at the receptor site. This was done by applying hierarchical principal component analysis (Hi-PCA) methods to the descriptors obtained from Grid.

A new procedure for improving the predictiveness of CoMFA models and its application to a set of dihydrofolate reductase inhibitors

A new automated procedure to improve the predictive quality of CoMFA models for both training and test sets is described. A model of greater consistency is generated by performing small reorientations of the underlying molecules for which too low activities are calculated. In order to predict activities of test compounds, the most similar molecules in the previously optimized model are identified and used as a basis for the prediction. This method has been applied to two independent sets of dihydrofolate reductase inhibitors (80 compounds each, serving as training sets), resulting in a significant increase of the cross-validated r2 value. For both models, the predictive r2 value for a test set consisting of 70 compounds was improved substantially.

Replacement of steric 6-12 potential-derived interaction energies by atom-based indicator variables in CoMFA leads to models of higher consistency

The steric descriptors commonly used in CoMFA--Lennard-Jones 6-12 potential-derived interaction energies calculated between a probe atom and the molecules under investigation--have been replaced by variables indicating the presence of an atom of a particular molecule in predefined volume elements (cubes) within the region enclosing the ensemble of superimposed molecules. The resulting 'atom indicator vectors' were used as steric fields in the subsequent PLS analyses, with and without inclusion of electrostatic Coulomb interaction-derived fields. Application of this method to five training sets (80 compounds each) and five test sets (60 compounds each), randomly selected from an ensemble of 256 dihydrofolate reductase inhibitors, leads to models of significantly higher consistency, as indicated by the cross-validated r2 values for the training sets and the predictive r2 values for the test sets.

Fundamentals of drug design from a biophysical viewpoint

Drug design means many things to many people. Commercially the aim is the development of compounds that can be patented and meet a variety of regulatory standards. In drug design, for medical purposes, toxicity and bio-availability are major considerations.