About Authors:
Rakesh Bhatia
School of Pharmaceutical Sciences, Department of Chemistry,
Jaipur National University, Jaipur-302025,
Rajasthan, India
ABSTRACT
QSAR has been applied extensively and successfully over several decades to find predictive models for activity of bioactive agents. QSAR have brought revolution in drug discovery process by thedevelopment of mathematicalrelationships linking chemical structures and pharmacological activity in quantitative manner of series of compound. The mathematical relationships between molecular descriptors and activity are used to find the parameters affecting the biological activity and/or estimate the property of other molecules. Description of the molecular structure, electronic orbital reactivity and the role of structural and steric components have been the subject of mathematical and statistical analysis. This review seeks to provide an evolution of QSAR and development of receptor theory.
Reference ID: PHARMATUTOR-ART-1210
INTRODUCTION
Various QSAR approaches have been developed gradually over a time span of more than a hundred years and served as a valuable predictive tool, particularly in the design of pharmaceuticals and agrochemicals. The QSPR (Quantitative Structure–Property relationship) acronymous is used when a property is modeled. The quantitative structure-activity relationship (QSAR) research field provides medicinal chemists with the ability to predict drug activity by mathematical equations which construct a relationship between the chemical structure and the biological activity. These mathematical equations are in the form of y = Xb + e that describe a set of predictor variables (X) with a predicted variable (y) by means of a regression vector (b). After the earlier QSAR studies by Hansch, who showed a correlation between biological activity and octanol-water partition coefficient, it is now assumed that the sum of substituent effects on the steric, electronic and hydrophobic interaction of compounds with their receptor determines their biological activity. The first step in constructing the QSAR models is finding one or more molecular descriptors that represent variation in the structural property of the molecules by a number. Nowadays, a wide range of descriptors are being used in QSAR studies which can be classified into different categories according to the Karelson approach including; constitutional, geometrical, topological, quantum, chemical and so on. There are different variable selection methods available including; multiple linear regression (MLR), genetic algorithm (GA), principal component or factor analysis (PCA/FA) and so on.
History of Q.S.A.R
It has been nearly 40 years since the quantitative structure-activity relationship (QSAR) paradigm first found its way into the practice of agrochemistry, pharmaceutical chemistry, toxicology, and eventually most facets of chemistry1.
Crum-Brown and Fraser2 (1868) expressed the idea that the physiological action of a substance in a certain biological system (EF) was a function (f) of its chemical composition and constitution (C).
EF = f C Equation [1]
Symbol EF means:CYRILLIC CAPITAL LETTER
Thus, an alteration in chemical constitution, ΔC, would be reflected by an alteration in biological activity ΔEF.
Richardson3 (1868) expressed the chemical structure as a function of solubility.
Mills4 (1884) developed a QSPR model for the prediction of melting and boiling points in homologous series, results were accurate to better than one degree.
Richet5 (1893) Correlated toxicities of a set of alcohols, ethers and ketones with aqueous solubility and showed that their cytotoxicities are inversely related to their corresponding water solubilities.
Overton and Meyer6,7(1897, 1899) correlated partition coefficients of a group of organic compounds with their anesthetic potencies and concluded that narcotic (depressant) activity is dependent on the lipophilicity of the molecules.
Development of Receptor Theory
The idea that drugs interacted with specific receptors began with Langley, who studied the mutually antagonistic action of the alkaloids, pilocarpine and atropine. He realized that both these chemicals interacted with some receptive substance in the nerve endings of the gland cells8. Paul Ehrlich defined the receptor as the binding group of the protoplasmic molecule to which a foreign newly introduced group binds9. In 1905 Langley’s studies on the effects of curare on muscular contraction led to the first delineation of critical characteristics of a receptor: recognition capacity for certain ligands and an amplification component that results in a pharmacological response10. Receptors are mostly integral proteins embedded in the phospholipid bilayer of cell membranes. Rigorous treatment with detergents is needed to dissociate the proteins from the membrane, which often results in loss of integrity and activity. Pure proteins such as enzymes also act as drug receptors. Their relative ease of isolation and amplification have made enzymes desirable targets in structure based ligand design and QSAR studies. Nucleic acids comprise an important category of drug receptors. Nucleic acid receptors (aptamers), which interact with a diverse number of small organic molecules, have been isolated by in vitroselection techniques and studied11.
Recent binary complexes provide insight into the molecular recognition process in these biopolymers and also establish the importance of the architecture of tertiary motifs in nucleic acid folding12. Groove-binding ligands such as lexitropsins hold promise as potential drugs and are thus suitable subjects for focused QSAR studies13.
It is now possible to isolate membrane bound receptors, although it is still a challenge to delineate their chemistry, given that separation from the membrane usually ensures loss of reactivity. Nevertheless, great advances have been made in this arena, and the three-dimensional structures of some membrane- bound proteins have recently been elucidated. To gain an appreciation for mechanisms of ligand-receptor interactions, it is necessary to consider the intermolecular forces at play. Considering the low concentration of drugs and receptors in the human body, the law of mass action cannot account for the ability of a minute amount of a drug to elicit a pronounced pharmacological effect. The driving force for such an interaction may be attributed to the low energy state of the drug-receptor complex: KD = [Drug] [Receptor] / [Drug-Receptor Complex]. Thus, the biological activity of a drug is determined by its affinity for the receptor, which is measured by its KD, the dissociation constant at equilibrium. A smaller KD implies a large concentration of the drug-receptor complex and thus a greater affinity of the drug for the receptor. The latter property is promoted and stabilized by mostly non-covalent interactions sometimes augmented by a few covalent bonds. The spontaneous formation of a bond between atoms results in a decrease in free energy; that is, ?Gis negative. The change in free energy ?G is related to the equilibrium constant Keq.
ΔG° = -RT ln Keq
Thus, small changes in ΔG0 can have a profound effect on equilibrium constants.
The seminal work of Hammett14,15 (1935, 1937) gave rise to the σ-ρ culture correlated the effect of the addition of a substituent on benzoic acid with the dissociation constant, postulated electronic sigma-rho constants and established the linear free-energy relationship (LFER) principle.
Hammett found that a linear relationship resulted when substitutions of different groups were made to aromatic compounds.
log K/ K0 = ρ log K’/ K0’ = ρ σ Equation [2]
K0 and K0’ are equilibrium constants for unsubstituted compounds and K and K’ are the equilibrium constants for substituted compounds. Hammett used benzoic acid as reference
compound yielding the σ. To interpret this equation, if the linear relation defines ρ > 1, then the effect of the substitutions is greater than making the same substitutions on benzoic acid. The σ describes the properties of the substitution groups. If σ is positive, the group is electron withdrawing. If σ is negative, the group is electron donating. The magnitude of σ indicates the degree of these effects.
In 1939, Ferguson16 correlated depressant action with the relative saturation of volatile compounds in their vehicle and introduced a thermodynamic generalization to the toxicity.
Bell and Roblin17(1942) Studied antibacterial activities of a series of sulfanilamides in terms of their ionizations.
Albert18 (1948) examined the effects of ionization/electron distribution and steric access on the potencies of a multitude of aminoacridines.
Taft19 (1952) Postulated a method for separating polar, steric, and resonance effects and introduced the first steric parameter, ES.
Hansch and Muir20 (1962) Correlated the biological activities of plant growth regulators with Hammett constants and hydrophobicity.
Using the octanol/water system, a whole series of partition coefficients were measured, and thus a new hydrophobic scale was introduced. The parameter π, which is the relative hydrophobicity of a substituent, was defined in a manner analogous to the definition of sigma21.
ΠX = log PX - log PH Equation [3]
PXand PH represent the partition coefficients of a derivative and the parent molecule, respectively.
The contributions of Hammett and Taft together laid the basis for the development of the QSAR
paradigm by Hansch and Fujita22 (1964), which combined the hydrophobic constants with Hammett’s electronic constants to yield the linear Hansch equation and its many extended forms.
Log 1/C = aσ + b π + ck Equation [4]
There is a consensus among current predictive toxicologists that Corwin Hansch is the founder of modern QSAR.
In the classic article23it was illustrated that, in general, biological activity for a group of ‘congeneric’ chemicals can be described by a comprehensive model:
Log 1/C50 = a π + b ε + c S + d Equation [5]
in which C, the toxicant concentration at which an endpoint is manifested (e.g. 50% mortality or effect), is related to a hydrophobicity term, p, (this is a substituent constant denoting the difference in hydrophobicity between a parent compound and a substituted analog, it has been replaced with the more general molecular term the log of the 1-octanol/water partition coefficient, log Kow), an electronic term, 1, (originally the Hammett substituent constant, s) and a steric term, S, (typically Taft’s substituent constant, ES). Due to the curvilinear, or bilinear, relationship between log1/C50 and hydrophobicity normally found in single dose tests the quadratic π2 term was later introduced to the model.
The rationale for Eq. (5) was given by McFarland24. He hypothesized that the relative activity of a biological active molecule, such as a toxicant, is dependent on: (1) the probability (Pr1) that the toxicant reaches its site of action, (2) the probability (Pr2) that the toxicant will interact with the target at this site, and (3) the external concentration or dose.
Hansch25 (1969) Developed the parabolic Hansch equation for dealing with extended hydrophobicity ranges.
Log 1/C= - a (log P) 2 + b.log P + c σ + k Equation [6]
The delineation of these models led to explosive development in QSAR analysis and related Approaches26.
Besides the Hansch approach, other methodologies were also developed to tackle structure-activity questions.
Free and Wilson27 (1964) formulated an additive model, where the activity is discretized as a simple sum of contributions from different substituents.
BA = Σ aixi + u Equation [7]
BA is the biological activity, uis the average contribution of the parent molecule, and aiis the contribution of each structural feature; xidenotes the presence Xi = 1 or absence Xi= 0 of a particular structural fragment.
In the years after the 1960s, the need to solve new problems, together with the contributions of many other investigators, generated thousands of variations of the Hansch approach to QSAR modelling, as well as approaches that are formally completely new.
Fujita and Ban28 (1971) simplified the Free-Wilson equation estimating the activity for the non-substituted compound of the series and postulated Fujita-Ban equation that used the logarithm of activity, which brought the activity parameter in line with other free energy-related terms.
Log BA = Gi Xi+ u Equation [8]
In this equation, uis defined as the calculated biological activity value of the unsubstituted parent compound of a particular series.Girepresents the biological activity contribution of the substituents, whereas Xiis ascribed with a value of one when the substituent is present or zero when it is absent.
Kubinyi29 (1976) Investigated the transport of drugs via aqueous and lipoidal compartment systems and further refined the parabolic equation of Hansch to develop a superior bilinear (non-linear) QSAR model.
Log 1/C =a. log P- b.log (β. P + 1) + k Equation [9]
Hans Konemann30 and Gilman Veith31 who in the early 1980s developed multi-class-based, hydrophobic- dependent models for industrial organic chemicals, must share credit for the revival of QSAR.
Hansch and Gao32 (1997) Developed comparative QSAR (C-QSAR), incorporated in the C-QSAR program.
Heritage and Lowis33, 34 (1997) Developed Hologram QSAR (HQSAR), where the structures are converted into all possible fragments, which are assigned specific integers, and then hashed into a fingerprint to form the molecular hologram. The bin occupancies of these holograms are used as the QSAR descriptors, encoding the chemical and topological information of molecules.
Cho and workers35 (1998) Developed Inverse QSAR, which seeks to find values for the molecular descriptors that possess a desired activity/property value. In other words, it consists of finding the optimum sets of descriptor values best matching a target activity and then generating a focused library of candidate structures from the solution set of descriptor values.
Labute36 (1999) Developed Binary QSAR to handle binary activity measurements from high-throughput screening (e.g., pass/fail or active / inactive), and molecular descriptor vectors as input. A probability distribution for actives and inactive is then determined based on Bayes’ Theorem.
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At present, the QSAR science, founded on the systematic use of mathematical models and on the Multivariate point of view is one of the basic tools of modern drug and pesticide design and has an increasing role in environmental sciences.
QSAR models exist at the intersection of chemistry, statistics and biology, in toxicological studies. The development of a QSAR model requires these three components: 1) a data set that provides experimental measures of a biological activity for a group of chemicals; 2) molecular structure and/or property data (i.e. the descriptors, variables, or predictors) for this group of chemicals; and 3) statistical methods, to find the relationship between these two data sets.
The limiting factor in the development of QSARs is the availability of high quality experimental data. In QSAR analysis, it is imperative that the input data be both accurate and precise to develop a meaningful model. In fact, it must be realized that any resulting QSAR model that is developed is only as valid statistically as the data that led to its development.
Data used in QSAR evaluations are obtained either from the literature or generated specifically for QSAR-type analyses. These data can consist of congeneric series of chemicals or assure structural diversity even within a chemical class. This diversity has allowed the generalization of more robust QSARs, applicable in an extended way. A structure– activity model is defined and limited by the nature and quality of the data used in model development and should be applied only within the model’s applicability domain.
The ideal QSAR should: (1) consider an adequate number of molecules for sufficient statistical representation, (2) have a wide range of quantified end-point potency (i.e. several orders of magnitude) for regression models or adequate distribution of molecules in each class (i.e. active and inactive) for classification models, (3) be applicable for reliable predictions of new chemicals (validation and applicability domain) and (4) allow to obtain mechanistic information on the modelled end-point. Chemical descriptor(s) include empirical, quantum chemical, or non-empirical parameters. Empirical descriptors may be measured or estimated and include physico-chemical properties (such as for instance log P). Non-empirical descriptors can be based on individual atoms, substituents, or the whole molecule, they are typically structural features. They can be based on topology or graph theory and, as such, they are developed from the knowledge of 2D structure, or they can be calculated from the 3D structural conformations of a molecule.
A variety of properties have been also used in QSAR modeling, these include physico-chemical, quantum chemical and binding properties. Examples of molecular properties are electron distribution, spatial disposition (conformation, geometry, and shape), and molecular volume. Physicochemical properties include descriptors for the hydrophobic, electronic, and steric properties of a molecule as well as other properties including solubility and ionization constants. Quantum chemical properties include charge and energy values. Binding properties involve biological macromolecules and are important in receptor-mediated responses.
A big problem related to molecular descriptors is their reproducibility: experimental values can differ greatly even when referred to the same compound37. Several approaches have been developed for the theoretical calculation of logP38-42, but also in these calculations it is not uncommon to have differences of several orders of magnitude43.
In modern QSAR approaches, it is becoming quite common to use a wide set of theoretical molecular descriptors of different kinds, able to capture all the structural aspects of a chemical to translate the molecular structure into numbers. Different descriptors are different ways or perspectives to view a molecule, taking into account the various features of its chemical structure, not only mono-dimensional as the simple counts of atoms and groups, but also bi-dimensional from the topological graph or three-dimensional from a minimum energy conformation. A lot of software calculates wide sets of different theoretical descriptors, from SMILES, 2D-graphs to 3D- x, y, z-coordinates. Some of the more used are mentioned here: ADAPT44, 45 OASIS46, CODESSA47, MolConnZ48, and DRAGON49. It has been estimated that more than 3000 molecular descriptors are now available, and most of them have been summarized and explained50-52. The great advantage of theoretical descriptors is that they can be calculated homogeneously by a defined software for all chemicals, even those not yet synthesized, the only need being a hypothesized chemical structure, thus they are reproducible.
Modeling methods used in the development of QSARs are of two types in relation to the modelled response: a potency of an end-point (a defined value of EC50) or a category/class (for instance Mutagen/Not mutagen).
For the potency modelling, the most widely used mathematical technique is multiple regression analysis (MRA). Regression analysis is a simple approach that leads to a result that is easy to understand and, for this reason, most QSARs are derived using regression analysis.
Regression analysis is a powerful means for establishing a correlation between independent variables (molecular descriptors X) and a dependent variable Y, such as biological activity:
Y= b + aX1 + c X2 + ….. Equation [10]
For the modelling of categories, different quantitative models of classification can be applied. A wide range of classification methods exists, including: discriminant analysis (DA; linear , quadratic, and regularized DA), SIMCA (Soft Independent Modeling of Class Analogy), k-NN (k-Nearest Neighbours), CART (Classification And Regression Tree), Artificial Neural Network, Support Vector Machine, etc. In these tecniques, the term “quantitative” is referred to the numerical value of the variables (the molecular descriptors) necessary to classify the chemicals in the qualitative classes. It is evident from the literature analysis that the QSAR world has undergone profound changes since the pioneering work of Corvin Hansch, considered the founder of modern QSAR modeling.
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The main change is reflected in the growth of a parallel and quite different conceptual approach to the modeling of the relationships among a chemical’s structure and its activity/properties.
In the Hansch approach, still applied widely and followed by many QSAR modelers, for instance Schultz et al.53 Veith and Mekenyan54, Benigni55, molecular structure is represented by only a few molecular descriptors (typically log Kow, Hammett constants, HOMO/LUMO, some steric parameters) selected personally by the modeler and inserted in the QSAR equation to model a studied end-point. Alternatively, in a different approach chemical structure is represented, in the first preliminary step, by a large number of theoretical molecular descriptors which are then, in a second step, selected by different chemometric methods as the best correlated with response and included in the QSAR model (the algorithm). The fundamental aim is the optimization of model performance for prediction.
According to the Hansch approach, descriptor selection is guided by the modeler’s conviction to have a prioriknowledge of the mechanism of the studied activity/property, and the presumption to assign mechanistic meaning to any used molecular descriptor selected by the modeler from among a limited pool of potential modeling variables, normally well known and repeatedly used (for instance: log Kow is a universal parameter miming cell membrane permeation, thus it is used in a lot of toxicity models, but it is also related to various partition coefficients such as bioconcentration/bioaccumulation, soil sorption coefficient, etc.; HOMO/LUMO are always selected for modeling chemical reactivity, etc.).
On the other hand, the ‘statistical’ or chemometric approach, an approach parallel to the previous so- called ‘mechanistic’ one, is based on the fundamental conviction that the QSAR modeler should not influence, a prioriand personally, the descriptor selection through mechanistic assumptions, but should apply unbiased mathematical tools to select, from a wide pool of input descriptors, those descriptors most correlated to the studied response. The number and typology of the available input descriptors must be as wide and different as possible in order to guarantee the possibility of representing any aspect of the molecular structure. Different descriptors are different ways or perspectives to view a molecule, however the models must be developed taking into account the principle of parsimony, named the Ockham‘s Razor : "entities should not be multiplied beyond necessity" or “avoid complexity if not necessary”. This principle is often paraphrased as "The simplest solution is the best."
Thus, descriptor selection must be performed by applying mathematical approaches (such as for instance evolutionary techniques, Genetic Algorithms, etc) with the final and crucial aim to maximize, as an optimization parameter, the predictive power of the QSAR model, as the real utility of any model is considered its predictivity.
Regarding the interpretability of the descriptors, it is important to take into account that modeled response is frequently the result of a series of complex biological or physico-chemical mechanisms, thus it is very difficult and reductionist to ascribe too much importance to the mechanistic meaning of the molecular descriptors used in a QSAR model. Moreover, it must also be highlighted that in multivariate models such as MLR models, even though the interpretation of the singular molecular descriptor can be certainly useful, it is only the combination of the selected set of descriptors that is able to model the studied end-point. If the main aim of QSAR modeling is to fill the gaps in available data, the modeler attention should be focused on model quality. In relation to this point, Livingstone states56: “The need for interpretability depends on the application, since a validated mathematical model relating a target property to chemical features may, in some cases, be all that is necessary, though it is obviously desirable to attempt some explanation of the “mechanism” in chemical terms, but it is often not necessary, per se". Zefirov and Palyulin57 took the same position, differentiating predictive QSARs, where attention essentially concerns the best prediction quality, from descriptive QSARs where major attention is paid to descriptor interpretability.
The first aim of any modeler should be validation for the predictive application of the QSAR model, for both the mechanistic approach and the statistical one. The best fit models are not the best ones for prediction. In fact, a QSAR model must, first of all, be a real model, robust and predictive, to be considered a reliable model; only a stable and predictive model can be usefully interpreted for its mechanistic meaning, even so this is not always easy or feasible.
QSAR model validation has been recognized by specific OECD expert groups as a crucial and urgent point in recent years, and this has led to the development, for regulatory purposes, of the
OECD principles for the validation of QSAR models 58. The need for this important action was mainly due to the recent new chemicals policy of the European Commission (REACH: Registration, Evaluation and Authorization of Chemicals) 59, that explicitly states the need to use (Q)SAR models to reduce experimental testing (including animal testing). Obviously, to meet the requirements of the REACH legislation it is essential to use (Q) SAR models that produce reliable estimates, i.e., validated QSAR models. Thus, reliable QSAR model must be associated with the following information: 1) a defined endpoint; 2) an unambiguous algorithm; 3) a defined domain of applicability; 4) appropriate measures of goodness-of–fit, robustness and predictivity; 5) a mechanistic interpretation, if possible.
The need for interpretability depends on the application, as a validated mathematical model relating a target property to chemical features may be all that is necessary, particularly when predicted data are needed for screening of large libraries of chemicals, though it is obviously desirable to attempt some explanation of the ‘mechanism’ in chemical terms.
Conclusion
QSAR has shown revolution from the expression by Crum-Brown and Fraser in 1868 to its computational revolution. It has also been applied to areas related to discovery and subsequent development of bioactive agents: distinguishing drug-like from non-drug-like molecules60, drug resistance61, toxicity prediction62-67, physicochemical properties prediction (e.g. water solubility, lipophilicity)68, gastrointestinal absorption69, activity of peptides70, data mining71, drug metabolism72 and prediction of other pharmacokinetic and ADME properties73,74.
REFERENCES
1. C. Hansch and A. Leo, Substituent Constants for Correlation Analysis in Chemistry and Biology, John Wiley & Sons, New York, 1979.
2. A. Crum-Brown, T.R. Fraser, Trans. R. Soc. Edinburgh 1868–1869, 25, 151.
3. Richardson, B.J. Physiological research on alcohols. Med. Times Gaz., 1868, 2, 703-706.
4. Mills, E.J. On melting point and boiling point as related to composition. Philos. Mag., 1884, 17, 173-187.
5. Richet, C. On the relationship between the toxicity and the physical properties of substances. Compt. Rendus Seances Soc. Biol., 1893, 9, 775-776.
6. Overton, E. Osmotic properties of cells in the bearing on toxicology and pharmacology. Z. Physik. Chem., 1897, 22, 189-209.
7. Meyer, H. On the theory of alcohol narcosis I. Which property of anesthetics gives them their narcotic activity? Arch. Exp. Pathol. Pharmakol., 1899, 42, 109-118.
8. J. Langley, J. Physiol., 1, 367 (1878).
9. P. Ehrlich, Klin. Jahr., 6, 299 (1897).
10. J. N. Langley, J. Physiol., 33, 374 (1905).
11. M. Famulok, Curr. Opin. Struct. Biol., 9, 324 (1999).
12. K. Y. Wang, S. Swaminathan, and P. H. Bolton, Biochemistry, 33, 7617 (1994).
13. J. W. Lown in S. Neidle and M.-J. Waring, Eds., Molecular Aspects of Anticancer Drug-DNA Interactions, Macmillan, Basinstoke, UK, 1993, p. 322.
14. Hammett, L.P. Some relations between reaction rates and equilibrium constants. Chem. Rev., 1935, 17, 125-136.
15. Hammett, L.P. The effect of structure upon the reactions of organic compounds. benzene derivatives. J. Am. Chem. Soc., 1937, 59, 96-103.
16. Ferguson, J. The Use of Chemical Potentials as Indices of Toxicity. Proc. R. Soc. Lond. B, 1939, 127, 387-404.
17. Bell, P.H.; Roblin, R.O. Studies in chemotherapy. vii. a theory of the relation of structure to activity of sulfanilamide type compounds1. J. Am. Chem. Soc., 1942, 64, 2905-2917.
18. Albert, A.; Goldacre, R.; Phillips, J. The strength of heterocyclic bases. J. Chem. Soc., 1948, 2240-2249.
19. Taft, R.W. Polar and steric substituent constants for aliphatic and o- Benzoate groups from rates of esterification and hydrolysis of esters1. J. Am. Chem. Soc., 1952, 74, 3120-3128.
20. Hansch, C.; Maloney, P.P.; Fujita, T.; Muir, R.M. Correlation of biological activity of phenoxyacetic acids with hammett substituent constants and partition coefficients. Nature, 1962, 194, 178-180.
21. G. Klopman, J. K. Li, S.Wang, M. Dimayuga, J. Chem. Inf. Comput. Sci. 1994, 34, 752.
22. Hansch, C.; Fujita, T. P. Analysis. A Method for the correlation of biological activity and chemical structure. J. Am. Chem. Soc., 1964, 86, 1616-1626.
23. C. Hansch, T. Fujita, J. Am. Chem. Soc. 1964, 86, 1616.
24. J.W. McFarland, J. Med. Chem. 1970, 13, 1092.
25. Hansch, C. Quantitative approach to biochemical structure-activity relationships. Acc. Chem. Res., 1969, 2, 232-239.
26. C. Hansch, A. Leo, Exploring QSAR, Fundamentals and Applications in Chemistry and Biology, ACS Professional Reference Book, American Chemical Society, Washington, DC, 1995.
27. Free, S.M., Jr.; Wilson, J.W. A Mathematical contribution to structure-activity studies. J. Med. Chem., 1964, 7, 395-399.
28. Fujita, T.; Ban, T. Structure-activity study of phenethylamines as substrates of biosynthetic enzymes of sympathetic transmitters. J. Med. Chem., 1971, 14, 148-152.
29. Kubinyi, H. Quantitative structure-activity relationships. IV. Nonlinear dependence of biological activity on hydrophobic character: a new model. Arzneimittelforschung, 1976, 26, 1991-1997.
30. H. Konemann, Toxicology, 1981, 19, 209.
31. G.D. Veith, D.J. Call, L.T. Brooke, Can. J. Fish Aquat. Sci. 1983, 40, 743.
32. Hansch, C.; Gao, H. Comparative QSAR: Radical reactions of benzene derivatives in chemistry and biology. Chem. Rev., 1997, 97, 2995-3060.
33. Hurst, T.; Heritage, T. HQSAR - A Highly Predictive QSAR Technique Based on Molecular Holograms. In: 213th ACS Natl. Meeting, San Francisco, CA, 1997.
34. Lowis, D.R. HQSAR: A New, Highly Predictive QSAR Technique. In: Tripos Technical Notes; Tripos Inc.: USA, Vol. 1, 1997.
35. Cho, S.J.; Zheng, W.; Tropsha, A. Rational combinatorial library design. 2. Rational design of targeted combinatorial peptide libraries using chemical similarity probe and the inverse QSAR approaches. J. Chem. Inf. Comput. Sci., 1998, 38, 259-268.
36. Labute, P. Binary QSAR: a new method for the determination of quantitative structure activity relationships. Pac. Symp. Biocomput., 1999, 444-455.
37. R. Renner, Environ. Sci. Technol. 2002, 36, 410A.
38. R. Mannhold, A. Petrauskas, QSAR Comb. Sci. 2003, 22, 466.
39. clogP.pomona.edu/medchem/chem/clogP/index.html
40. syrres.com/esc/kowwin.html
41. vcclab.org
42. G. Klopman, J. K. Li, S.Wang, M. Dimayuga, J. Chem. Inf. Comput. Sci. 1994, 34, 752.
43. E. Benfenati, G. Gini, N. Piclin, A. Roncaglioni, M. R. Var?`, Chemosphere, 2003, 53, 1155.
44.A.J. Stuper P.C., Jurs, J Chem Inf Compu Sci, 1976, 16, 99
45. http://research.chem.psu.edu/pcjgroup/ADAPT.html.
46. O. Mekenyan, D. Bonchev, , Acta Pharm Jugosl., 1986, 36, 225.
47. A.R., Katritzky, V.S., Lobanov, CODESSA, Version 5.3, University of Florida, Gainesville, 1994.
48. MolConnZ, Ver. 4.05, 2003, Hall Ass. Consult., Quincy, MA.
49. R. Todeschini, V. Consonni, A. Mauri, M. Pavan, DRAGON—Software for the calculation of molecular descriptors. Ver. 5.4 for Windows, 2006, Talete srl, Milan, Italy.
50. J. Devillers, A.T Balaban, (Eds.) Topological Indices and Related Descriptors in QSAR and QSPR, Amsterdam: Gordon Breach Sci. Pub., 1999.
51. M. Karelson, Molecular Descriptors in QSAR/QSPR. New York: Wiley-InterScience, 2000.
52. R. Todeschini, V. Consonni, Handbook of Molecular Descriptors, Wiley-VCH, Weinheim (Germany), 2000.
53. T.W Schultz, M.T.D.Cronin, T.I. Netzeva , A.O. Aptula, , Chem. Res. Toxicol., 2002, 15, 1602.
54. G.D Veith, O.G. Mekenyan, , Quant. Struct-Act. Rel., 1993, 12, 349
55. R. Benigni, Chem. Rev., 2005, 105, 1767.
56. D.J. Livingstone, J. Chem. Inf. Comput. Sci., 2000, 40, 195
57. N.S. Zefirov and V.A. Palyulin, J. Chem. Inf. Comput. Sci., 2001, 41, 1022
58. oecd.org/document/23/0,2340,en_2649_201185_33957015_1_1_1_1,00.html.
59. europa.eu.int/comm/environment/chemicals/reach.html.
60. Ajay, Walters, W.P. and Murcko, M. (1998), ‘Can we learn to distinguish between “drug-like” and “non-drug-like” molecules?’, J. Med. Chem., Vol.41, pp. 3314-3324.
61. Wiese, M. and Pajeva, I.K. (2001), ‘Structure- activity relationships of multidrug resistance reversers’, Curr. Med. Chem., Vol. 8(6), pp. 685-713.
62. Schultz, T.W. and Seward, J.R. (2000), ‘Health effects related structure-toxicity relationships – a paradigm for the first decade of the new millenium’, Sci. Total Environ., Vol.249(1-3), pp. 73-84.
63. Benigni, R., Giuliani, A., Franke, R. and Gruska, A. (2000), ‘Quantitative structure-activity relationships of mutagenic and carcinogenic aromatic amines’, Chem. Rev., Vol.100(10), pp.3697-3714.
64. Garg, R., Kurup, A. and Hansch, C. (2001), ‘Comparative QSAR: On the toxicology of the phenolic OH moiety’, Crit. Rev. Toxicol., Vol. 31(2), pp. 223-245.
65. Bashir, S.J., and Maibach, H.I. (2000), ‘Quantitative structure analysis relationships in the prediction of skin sensitization potential’, Biochem. Modulation Skin React., pp. 61-64
66. Cronin, M.T.D. (2000), ‘Computational methods for the prediction of drug toxicity’, Curr. Opin. Drug Discovery Dev., Vol.3(3), pp. 292-297.
67. Freidig, A.P. and Hermens, J.L.M. (2001), ‘Narcosis and chemical reactivity QSARs for acute fish toxicity’, Quantitative structure-activity relationships, Vol. 19(6), pp. 547-553.
68. Gombar, V.K. and Enslein, K. (1996), ‘Assessment of n-octanol/water partition coefficient: When is the assessment reliable?’ J. Chem. Inf. Comput. Sci., Vol. 36(6), pp. 1127-1134.
69. Agatonovic- kustrin, S., Beresford, R., Yusof, A. and Pauzi. M. (2001), ‘Thereotically- derived molecular descriptors important in human intestinal absorption’, J. Pharm. Biomed. Anal.,Vol.25(2),pp.227-237.
70. Brusic, V., Bucci, K., Schonbach, C. et al. (2001), ‘Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding’, J. Mol. Graph. Modell., Vol. 19(5),pp.405-411
71. Burden, F.R. and Winkler, D.A. (2000), ‘The computer simulation of high throughput screening of bioactive molecules’, Mol. Model. Predict. Bioact, [Proceedings of the 12th European Symposium on Quantitative Structure-Activity Relationships], pp.175-180.
72. Lewis, D.F.V. (2000), ‘Structural characteristics of human P450s involved in drug metabolism: QSARs and lipophilicity profiles’, Toxicology, Vol. 144(1-3), pp. 197-203.
73. Vedani, A. and Dobler, M. (2000), ‘Multi-dimensional QSAR in drug research: predicting binding affinities, toxicity and pharmacokinetic parameters’, Prog. Drug Res., Vol. 55, pp. 105-13
74. Guba, W. and Cruciani, G. (2000), ‘Molecular field-derived descriptors for the multivariate modeling of pharmacokineic data’, Mol. Model. Predict. Bioact., [Proceedings of the 12th European Symposium on Quantitative Structure-Activity Relationships], pp.89-94.
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