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Volume 16, Number 24,2010

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Current Pharmaceutical Design, 2010, 16, 2640-2655

Drug Discovery and Design for Complex Diseases through QSAR Computational Methods Cristian R. Munteanu*,1, Enrique Fernández-Blanco1, José A. Seoane1, Pilar Izquierdo-Novo2, José Ángel Rodríguez-Fernández3, José María Prieto-González4 , Juan R. Rabuñal1 and Alejandro Pazos1 1 Department of Information and Communication Technologies, Computer Science Faculty,University of A Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain, 2Centro Oncológico de Galicia, Calle Monserrat s/n, 15999 A Coruña, Spain, 3Servicio de Cardiología, Complejo Hospitalario Universitario de A Coruña, Xubias de Arriba 84, 15006 A Coruña, Spain, 4Servicio de Neurología, Hospital Clínico de Santiago de Compostela, Spain

Abstract: There is a need for the study of complex diseases due to their important impact on our society. One of the solutions involves the theoretical methods which are fast and efficient tools that can lead to the discovery of new active drugs specially designed for these diseases. The Quantitative Structure - Activity Relationship models (QSAR) and the complex network theory become important solutions for screening and designing efficient pharmaceuticals by coding the chemical information of the molecules into molecular descriptors. This review presents the most recent studies on drug discovery and design using QSAR of several complex diseases in the fields of Neurology, Cardiology and Oncology.

Keywords: Drug design, QSAR, graphs, complex network, complex disease. INTRODUCTION The complex diseases are a major cause of disability and death worldwide and involve DNA single nucleotide polymorphisms (SNPs), post-translational protein modifications and environmental influences [1, 2]. The most common complex diseases are Alzheimer’s disease, Parkinson’s disease [3, 4], coronary artery disease [5], and several types of cancer [6]. The rare Mendelian disorders are relatively well characterized but little progress has been made in the discovery of common gene variations that predispose to complex diseases [7]. The complexity of these problems began to be studied more frequently with the graphical methods of information processing such as the complex network/graphs theory [8, 9]. Any real complex system such as drugs, proteins, nucleic acids, metabolisms, diseases or societies can be numerically characterized and compared according to the relationship properties between its components. Thus, the graphical methods become an efficient tool to describe complex networks made out of nodes such as atoms linked by chemical bonds (drug), amino acids connected by peptide chemical bonds (protein), nucleic bases linked by phosphate bonds (DNA/RNA), protein/genes/inter-mediates linked by a transformation or interaction (metabolism and diseases) or persons connected by a common activity (society). The invariant macromolecular descriptors named topological indices (TIs) or connectivity indices (CIs) [10] code the internal information about the structure of a complex system within the Graph or Complex Network (CN) theory. Some example of interesting TI/CI studies are applied to molecular graphs [11], Proteomics [12, 13], Enzymology [14-18], DNA/protein structures [19-26] [27, 28], drug-target interactions [20, 29], biochemical networks [30], reaction [31], metabolism [32], protein-protein interaction networks [33-38], enzymecatalyzed reactions [39], protein folding kinetics [16] and human serum proteome for cancer diagnosis and screening for cancerrelated molecules in the case of CRC [40, 41], breast [40, 42] and prostate [43, 44] cancers. Therefore, the Quantitative Structure Activity Relationship (QSAR) is widely-used for the prediction of *Address correspondence to this author at the Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, S/N, 15071 A Coruña, Spain; Tel: +34 981 167 000; Ext: 1302; Fax: +34 981 167 160; E-mail: [email protected]

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drug properties [45] and the Quantitative Protein (or Proteome)Disease Relationships (QPDRs) [8, 20, 46-50] for disease prediction. QSAR (sometimes QSPR: quantitative structure-property relationship) represents the method to quantitatively correlate the chemical structure with the biological activity or chemical reactivity [Activity = f(physio-chemical properties and/or structural properties)]. In addition, the physicochemical properties or structures are expressed by numbers and can form a mathematical relationship, or quantitative structure-activity relationship with the TIc/CIs. The mathematical models obtained can then be used to predict the biological activity of other chemical structures. This review presents the most important studies of drug discovery and design for complex diseases in the fields of Neurology, Cardiology and Oncology using QSAR models. GRAPHS AND QSAR A real network is a collection of objects connected by physical links (a computer network) or common properties (a social network). The graph is the abstract representation of a real network and it is made up of a collection of vertices (nodes) and edges (links) that connect pairs of nodes used to model pairwise relations between objects from a certain collection. The graph is undirected if there is no distinction between two vertices or directed if there is a difference between two linked nodes. In the case of the undirected graph of the drug molecules, the nodes are the atoms connected by chemical bonds (Fig. 1). In contrast, the metabolic pathways and the ontologies are directed graphs (digraphs) where the nodes are different molecules such as proteins, nucleic acids or cofactors linked by biochemical transformations and words connected by definition relationships. Any graph corresponds to one connectivity matrix, one vector of node degrees and one distance matrix [51]. These mathematical elements are the base for the calculation of the TIs and CIs for the considered system. In the case of the drug molecules, additional physical and chemical properties of the atoms and molecules are added as weights of the corresponding nodes such as lipophilicity, polarizability, electronic and steric properties [52-57]. 3D QSAR methods are more advanced when considering the threedimensional structure and the binding modes of protein ligands. An example of complex bio-systems that can be coded in TIs/CIs are the protein primary/secondary/ 3D structure, the Blood Serum Proteome Mass Spectrum, medical device outputs such as electroencephalogram, DNA/RNA nucleotide sequences/3D structure, © 2010 Bentham Science Publishers Ltd.

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Fig. (1). Drug molecule graph with atoms as nodes linked by chemical bonds (A), protein star-like graph with amino acids as nodes linked by peptide chemical bonds (B) and gene network with genes as nodes linked by regulation relations (C).

mRNA microarrays and Single Nucleotide Polymorphism sequences (SNPs) [58]. The first QSAR study was carried out in 1863, when A.F.A. Cros observed that the toxicity of alcohols to mammals increased as the water solubility of the alcohols decreased [59]. Later, in the 1890's, Hans Horst Meyer noted that the toxicity of organic compounds depended on their lipophilicity [59, 60]. Louis Hammett (1894-1987) added some QSAR studies that correlated electronic properties of organic acids and bases with their equilibrium constants and reactivity [61]. Hammett observed that adding substituents to the aromatic ring of benzoic acid had an orderly and quantitative effect on the dissociation constant.

A new approach was proposed by Cramer et al. [62] to describe the molecular properties for each field, calculated in a regular grid. Vectors have been extracted from these fields by using the principal component analysis and have been correlated with the biological activity. The method was called DYLOMMS (dynamic latticeoriented molecular modeling system). After a very slow development of grid-based 3D QSAR approaches, Cramer et al. [63] published in 1988 the method of comparative molecular field analysis (CoMFA). This molecular field-based method constituted the first real 3D QSAR method and it is more appropriate to describe ligand-receptor interactions, because it takes into consideration the properties of the ligands in their bioactive conformations. Thus, 3DQSAR refers to the application of force field calculations requiring

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three-dimensional structures (protein crystallography or molecule superposition) and using computed potentials (Lennard-Jones potential) rather than experimental constants and dealing with the overall molecule rather than with a single substituent. This method examines the steric fields (shape of the molecule) and the electrostatic fields based on the applied energy function [64]. Klebe et al. [65] introduced in 1994 the comparative molecular similarity indices (CoMSIA), a method of 3D-QSAR analysis, in which, by using a common probe atom, similarity indices are calculated at regularly placed grid points for the pre-aligned molecules. This new method is not sensitive to the changes in the orientation of the superimposed molecules in the lattice, and the resulted correlation can be graphically interpreted in terms of the field contribution maps. This allows the physicochemical properties that are relevant for binding to be easily mapped back on to molecular structures [66]. One of the modern versions of the QSAR method is the hologram QSAR (HQSAR), a 2D QSAR method that has shown a predictive ability comparable to those of more sophisticated 3D QSAR techniques. HQSAR generates specialized molecular holograms that incorporate information about each 2D fragment (i.e., linear, branched, and overlapping), and each of its constituent subfragments, implicitly encoding 3D structural information that is important for the binding affinity [67, 68]. TIs/CIs can be calculated with applications such as MARCHINSIDE [69], DRAGON [70], S2SNet [71], Centibin [72] and Pajek [73]. These tools calculate more than 1500 indices classified in the following main groups: constitutional descriptors [10], topological descriptors [74-81], walk and path counts [82-85], connectivity descriptors [86], information indices [87, 88], 2D autocorrelations [89], edge adjacency indices [87, 90, 91], Burden eigenvalue descriptors [92, 93], topological charge indices [94], eigenvaluebased indices [95], Randic molecular profiles [96], geometrical descriptors [10, 97, 98], RDF descriptors [99], 3D-MoRSE descriptors [100], WHIM descriptors [101], GETAWAY descriptors [102], atom-centered fragments [103], charge descriptors [104], molecular properties [10], 2D binary fingerprints [105] and 2D frequency fingerprints [105]. QSAR in Neurology Cathepsin B is a potential target for the development of drugs to treat several important human diseases and disorders such as neurodegenerative disorders, cardiovascular diseases, cancer, inflammation, rheumatoid arthritis, and Alzheimer’s disease [106-108]. Most of the cathepsin inhibitors disable their biological activity by forming irreversible covalent chemical bonds in the catalytic site of the enzyme. These irreversible inhibitors include dipeptidyl nitriles (Fig. 2A), vinyl sulfones, expoxysuccinates, acyloxymethyl ketones, fluoromethyl ketones, hydrazides, and bis-aamidoketones [109]. Recently, Zhou et al. [110] have proposed traditional continuous and binary QSAR models to predict cathepsin B inhibition using small molecules, to classify the biological activities of previously identified compounds and to distinguish active compounds from inactive compounds for drug development based on the calculated molecular and physicochemical properties. The regression correlation coefficients (r2) and the cross-validated correlation coefficients (q2) for continuous QSAR models have been 0.77 and 0.61 for all compounds, and 0.82 and 0.68, respectively, showing a strong correlation. The leave-one-out (LOO) method validated the models through the training-test set method and showed a strong level of predictability in distinguishing the active compounds from inactive compounds with accuracies of 0.89 and 0.94 for active and inactive compounds, respectively, in non-cross-validated models. Similar results were obtained for the cross-validated models. The results demonstrate the QSAR models’ ability to discriminate between active and inactive compounds and propose that models should be used for pre-screen compounds to facilitate compound optimization and to design novel inhibitors for drug development.

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder affecting the elderly population and it is characterized by the presence of lesions both at an intracellular (neurofibrillary tangles) and extracellular (amyloid plaques) level. The neurofibrillary tangles are made out of paired helical filaments, aggregates of phosphorylated protein tau that form [111, 112]. The amyloid plaques are deposits made out primarily of -amyloid insoluble peptides of approximately 4 kDa, generated from the precursor amyloid precursor protein (APP), a type of Ia transmembrane protein [113, 114]. In the AD pathology, the production of -amyloid peptide is the result of APP amyloidogenic processing and involves the first activity of -then of -secretase, and requires the internalization of APP from the plasma membrane to the endosomes and the lysosomes [115, 116]. -secretase catalyses the final step in A production and determines the length of A variants. Therefore, this protease has been a prime target for the development of potential therapeutic agents for AD. Sammi group [117] studied a 3D QSAR model using a series of 67 benzodiazepine analogues (Fig. 2B) reported as -secretase inhibitors and the molecular field analysis (MFA), with G/PLS to predict steric and electrostatic molecular field interaction for the activity. The pIC50 of compounds was considered as dependent variables whereas molecular field descriptors such as steric (CH3 ) and electrostatic (H+) as independent variables. G/PLS was carried out over 50,000 generations with a population size of 100. The MFA study was carried out using a training set of 54 compounds having r2 value of 0.858 and cross validated coefficient, r2cv value as 0.790. The results provided insight into the possible modification of the molecules for a better activity. Glycogen Synthase Kinase-3 (GSK-3) is a ubiquitously expressed serine/threonine protein kinase, originally identified as one of the protein kinases that phosphorylate GS [118] and linked to AD-associated abnormalities via hyperphosphorylation of the microtubule-associated protein tau [119]. Studies have indicated that the isoform GSK-3b facilitates APP processing, resulting in an increased production and aggregation of Amyloid-b (Ab) peptides [120]. The inhibition of GSK-3 with potent selective small molecules has been shown to protect primary neurons from the death induced by reduced PI-3 kinase activity [121]. Lather et al. [122] have carried out a QSAR study on indirubin (Fig. 2C) derivatives, reported as potent GSK-3b inhibitors, using molecular descriptors calculated by CODESSA [123] and Molconn-Z [124], followed by a regression analysis using Heuristic and best multilinear regression approaches [125]. The same set of inhibitors have been used to develop a novel 3D-QSAR method based on the principle of the alignment of pharmacophoric features employing the PHASE module of Schrödinger suite [126]. QSAR models were generated using 36 molecules and statistically significant for 2-D (r2=0.93) and 3-D (r2=0.97) in the training set. The predictive ability of both models was determined using a randomly chosen test set of eight molecules which gave predictive correlation coefficients (r2pred) of 0.6 and 0.91, for 2-D and 3-D models respectively, indicating a good predictive power. This relation between the crystallographic data and pharmacophore hypothesis confirmed the preferential binding mode of indirubins inside the active site. These studies demonstrated that 3D-QSAR model for the indirubins are better compared to 2DQSAR model. AD is characterized by widespread loss of central cholinergic neuronal function [127]. The only symptomatic treatment proven to be effective up to date is the use of cholinesterase inhibitors (ChEI) that increase the cholinergic activity [128, 129]. There are two types of ChE enzyme in the Central Nervous System (CNS): acetylcholinesterase (AChE; EC 3.1.1.7) and butyrylcholinesterase (BuChE; EC 3.1.1.8). AChE and BuChE share 65% amino acid sequence identity despite being encoded by different genes on human chromosomes 7(7q22) and 3(3q26), respectively [130]. AChE is responsible for the hydrolysis of acetylcholine at the syn-

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Fig. (2). The three-dimensional structure coordinate of cathepsin protein bound with dipeptidyl nitrile (DPN) (A); the bold-faced portion of most active molecule 1 was used as template for the superposition of the rest of the molecules (benzodiazepine analogues) (B); butyrylcholinesterase (BuChE) (C).

aptic cleft and the neuromuscular junction in response to the nerve action potential [131]. BuChE acts on butyrylcholine and hydrolyzes acetylcholine [132]. Both enzymes are able to catalyze the hydrolysis of acetylcholine (ACh) at a rate of >10,000 molecules per second [133]. Unlike AChE, BuChE is mainly derived from glia in the brain [134]. BuChE (see Fig. 2D) is relatively abundant in plasma and it can degrade a large number of ester-containing compounds, playing important pharmacological and toxicological roles [135] such as a potential detoxifying enzyme against the neurotoxic organophosphates [136, 137]. There is much evidence showing the relationship of the AChE function and impairments with Parkinson’s disease [138], Huntington’s disease [139], myasthenia gravis [140], schizophrenia [141], glaucoma [142], multiple sclerosis (MS) [143], and especially Alzheimer’s disease (AD) [127]. Tacrine, galantamine, rivastigmine, donopezil and huperzine are cholinesterase inhibitors, promising drugs currently used for the treatment of Alzheimer’s disease [144], but their clinical use is strictly limited because of several adverse effects such as hepatotoxicity and some pharmacokinetic disadvantages. Therefore, there is a need for searching for new more effective compounds as cholinesterase inhibitors. Zaheer-ul et al. [145] studied the BuChE inhibitors by using receptor-based modeling and 3D-QSAR and the genetic algorithm. 3D-QSAR models have been constructed using CoMFA and CoMSIA for a series of structurally related steroidal alkaloids as BuChE inhibitors. The most probable binding mode of the inhibitors into the BuChE active site has been obtained with docking studies. Multiple conformations were derived using the FlexS program in order to produce a more reliable 3D-QSAR model. The selection of the best conformation for CoMFA was performed by using the genetic algorithm. Both CoMFA and CoMSIA yielded significant cross-validated q2 values of 0.701 and 0.627 and the r2 values of 0.979 and 0.982, respectively. The comparison of contour maps for CoMFA and CoMSIA pointed out the structural requirements for the inhibitors and can be used as a basis for the design of the next generation inhibitor analogues. These results demonstrate the power

of the combination between the ligand-based and receptor-based modeling and genetic algorithm in order to build 3D-QSAR models for drug discovery and development for Alzheimer’s disease. Another family of drugs that present inhibitory activity towards both AChE and BChE are N-aryl derivatives [146]. Solomon et al. [147] carried out a QSAR based on a series of 88 N-aryl derivatives which display a varied inhibitory activity AChE and BChE. QSAR models were derived by a genetic function approximation (GFA) technique using topological, molecular shape, electronic and structural descriptors. The predictive ability of the QSAR model was evaluated using a test set of 26 compounds for AChE (r2pred = 0.857), (q2 = 0.803) and 20 compounds for BChE (r2pred = 0.882), (q2 = 0.857). The results pointed out that AlogP98, Wiener, Kappa1-AM, Dipole-Mag, and CHI-1 are the important descriptors effectively describing the bioactivity of the compounds. Recently, Takahashi team [148] has designed and analyzed with the QSAR method new BChE inhibitors such as N1-substituted norcymserine derivatives (Fig. 3A). The model is based on 18 compounds and evaluates the pIC50 with r2 of 0.980 using the log of the aqueous solubility, the Wiener polarity number, the sum of the van der Waals surface area of atoms where the atomic partial charges are ranging from -0,05 to 0, and the number of violations of Oprea’s lead-like test [149]. By synthesizing various norcymserine derivatives substituted on the N1 moiety, and by performing the QSAR study, they demonstrated that the pyridinylethyl and piperidinylethyl group substitution increased the anti-BuChE activity, and that the ionizable nitrogen of the substituent might contribute to this improvement. Asadabadi et al. [150] propose a combinatorial feature selection approach to describe the QSAR of dual site inhibitors of AChE by using a dataset of Munoz-Ruiz et al. [151] that includes 24 dual binding site AChE inhibitors with a unique core structure (see Fig. 3B). Thus, the molecules contain a tacrine and an indole ring linked together by a hydrocarbon linker chain, allowing a dual mechanism of inhibition to block both the active and peripheral sites of the enzyme. The QSAR model was based on 60 descriptors and a linear

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discriminant analysis (LDA) method [152], binary logistic regression (BLR) [153] and artificial neural networks (ANNs) [154-156]. The selection of the descriptors (constitutional, topological, Gálvez topological charge indices, empirical, molecular walk counts, atomcentered fragments, 2D autocorrelations, BCUT) was performed with a genetic algorithm-based neural network. The classification was improved from 70.83% for the mixed LDA and BLR model to 93.05% for the ANN model. The research has been studied with the QSAR method and the AChE inhibitors from the group of the carbamate derivates such as mono- and dialkyl 3-(1-dimethylamino ethyl)-phenyl carbamates [157, 158]. The authors showed a close correlation between the amount of energy needed to overcome the restriction of rotation about the amide bond and the affinity of the inhibitor for AChE. An anomalous ‘‘ethyl’’ effect explained that the diethyl carbamoyl derivative is the least active in a series of 5-(1,3,3-trimethylindolinyl) carbamates, about 7400 times less potent (Ki) than the dimethylcarbamoyl analogues [159]. Roy et al. carried out systematic 3D-QSAR studies (CoMFA, advance CoMFA and CoMSIA) on a series of 78 carbamate derivates (52 and 26 molecules in training and test set, respectively) (see Fig. 4). CoMFA (q2 = 0.733, r2 = 0.967, r2pred = 0.732) and CoMSIA (q2 = 0.641, r2 = 0.936, r2pred = 0.812) are highly predictive models and explain the variance in binding affinities both for the training and the test set compounds. The results confirm that steric, electrostatic and hydrophobic interactions play an important role in describing the variation in binding affinity. Neurodegenerative disorders are consequences of progressive and irreversible loss of neurons due to unwanted apoptosis, which involves caspases, a group of cystein proteases that cleave other proteins and inactivate them. The most important in apoptosis is caspases-3. Sharma et al. [160] proposed 1,3-dioxo-4-methyl-2,3dihydro-1hpyrrolo[3,4-c]quinolines [161] as caspase-3 inhibitors by a QSAR study using WIN CAChe 6.1 and Medicinal Chemistry Regression Machine. The best QSAR model was based on 25 compounds and the following descriptors: conformational minimum energy (CME), the zeroth-order connectivity index (0), the firstorder connectivity index (1), the second-order connectivity index (2), the dipole moment (), the electron affinity (EA), the total energy in the current geometry after structure optimization (TE), the heat of formation in the current geometry after optimization of structure (HF), the highest occupied molecular orbital energies (EHOMO), the lowest unoccupied molecular orbital energies (ELUMO),

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Fig. (4). AChE inhibitors: 1-aminoindane (A), 1- or 2-aminotetraline (B, C), and phenethylamine carbamates (D).

the octanol–water partition coefficient (log P), the molar refractivity(MR), the ionization potential (IP), the order 1 shape index (1), the order 2 shape index (2), the order 3 shape index (3), the zeroth-order valance connectivity index (0v), the first-order valance connectivity index (1v), and the second-order valance connectivity index (2v). The best model has r = 0.951, F = 65.62, SEE = 0.4175, t = 2.08. The results demonstrate that, if the partition coefficient (log P), the conformational minimum energy (CME), and the lowest unoccupied molecular orbital energy (ELUMO) are altered, the biological activity is increased. A series of QSAR studies have been carried out, having as targets different biomolecules involved in neurological diseases such as: serotonin 5-HT6 receptor antagonists for the treatment of Alzheimer's disease [162], 3D-QSAR study of corticotropinreleasing factor 1 antagonists and pharmacophore-based drug design [163], QSAR analysis of pyrazolidine-3,5-diones derivatives as dual specificity tyrosine (Y) phosphorylation regulated kinase 1A (Dyrk1A) inhibitors for Down syndrome treatment [164], molecular modeling of histamine H3 receptor and QSAR studies on arylben-

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zofuran derived H3 antagonists in Alzheimer’s and Parkinson’s diseases [165], 3D-QSAR and QSSR studies of 3,8-diazabicyclo [4.2.0]octane derivatives as neuronal nicotinic acetylcholine receptors by comparative molecular field analysis (CoMFA) [166], 3-D QSAR study of catechol-O-methyltransferase inhibitors using CoMFA and CoMSIA [167], and application of validated QSAR models of D1 dopaminergic antagonists for database mining [168].

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extra reduction of the descriptors was carried out using BuildQSAR software [176] that searches for multiple linear regressions (MLR) models of up to four variables with correlation coefficients r2 > 0.70. The best models were the classic 2D QSAR (r2 = 0.76, q2 = 0.72) and the hologram QSAR model (r2 = 0.88, q2 = 0.70). Hypertension is a constant state of abnormally high pressure in the arteries that can generate other cardiac diseases. There are two groups of populations divided according to the cause of hypertension: between 85% and 90% of people suffer from primary hypertension with no known cause and 10–15% of people have secondary hypertension caused by renal, endocrine or pregnancy related diseases. The complications can involve heart enlargement and failure, renal dysfunction and cerebrovascular accidents. The sympathetic division of the autonomic nervous system and the kidneys are the body mechanisms that control blood pressure. Therefore, the drugs used against hypertension are diuretics, adrenergic blockers, centrally acting alpha-agonists, angiotensin-converting enzyme (ACE) inhibitors, angiotensin II blockers, calcium channel blockers, direct vasodilators and 5-HT antagonists [177]. The main vascular function of 5-HT is its ability to act as a vasoconstrictor. The 5-HT2A, 5-HT2B and 5-HT1B receptors have been involved as mediators of 5-HT-induced contraction in the vascular smooth muscle [178, 179]. Among several tested antihypertensive compounds, 1-[3-(4-acetamidophenylthio)propyl]-4-[3-methyl-phenyl] piperazine [180] emerged as a potential drug due its activity comparable to centhaquin [181]. Saxena et al. [182] proposed a QSAR studies on 42 hypotensive 1-[3-(4-substituted phenylthio) propyl]4-(substituted phenyl) piperazines (see Fig. 5B). The results demonstrated that resonance and hydrophobic parameters of the aryl substituents are important for the hypotensive activity. The similar role of resonance parameter in describing the variance of 5-HT2A receptor binding affinities of these compounds suggests a possible

QSAR in Cardiology The coronary heart disease (CHD) mortality decreased in Western Europe and North America during the last decades but still remains one of the major causes of human death [169, 170]. The most successful therapeutic approach for the treatment of this disease is based on the reduction of low density-lipoprotein cholesterol (LDL-C) levels [171] by the use of statins [172]. The epidemiologic studies have identified that low levels of high density-lipoprotein cholesterol (HDL-C) are a higher risk for CHD than LDL-C, total cholesterol or plasma triglycerides (TG) [173]. Thus, the next target in the CHD treatment relies on increased HDL-C levels [174] by the inhibition of the cholesteryl ester transfer protein (CETP), a glycoprotein that binds to HDL and it is involved in the transfer of lipoprotein particles and neutral lipids, including cholesteryl ester, phospholipids, and triglyceride. Castilho et al. [175] used 2DQSAR on a series of 85 CETP inhibitors (N-N-disubstituted trifluoro-3-amino-2-propanol derivatives, Fig. 5A). For the classic 2D QSAR, molecular descriptors have been calculated, such as connectivity indices, 2D autocorrelation descriptors, and Burden eigenvalues (independent variables) by using DRAGON 5.4 software. The 929 descriptors have been subjected to the following selection strategy. Descriptors possessing constant values as well as those with poor correlation to biological property (r2 < 0.10) or those which are more than 0.99 correlated were discarded. The

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of diseases like angina pectoris, hypertension, congestive heart failure, myocardial salvage in myocardial infarction (MI), antihypoglycemia (insulinoma), alopecia, bronchial asthma, urinary urge incontinence, Raynaud’s disease, etc. [190]. Alam et al. [191] carried out a QSAR modeling of pancreatic -cell KATP channel openers R/S-3,4-dihydro-2,2-dimethyl-6-halo-4-(substituted phenylaminocarbonylamino)-2H-1-benzopyrans using MLR-FA techniques. These compounds are a new series of ATP-sensitive potassium (KATP-p) channel openers selective towards pancreatic -cells. The QSAR model was based on 23 chemical structures. The predictor variables were WangeFord charges, partition coefficient, molar refractivity, principle moment of inertia at X, Y and Z axes and the response variable was the logarithm of percentage of residual insulin secretion. k-Mean cluster analysis was performed to design training and test set. Multiple linear regressions (MLR) [192] with factor analysis were performed to develop four QSAR equations using different combinations of the predictor variables based on factor loadings (r2pred between 0.891 and 0.910). Regression coefficients of all descriptors used are significant at more than 95% level. The results demonstrated the importance for the inhibition of residual insulin secretion of the WangeFord charges on atom numbers 11, 17, 18, 19 and 21 (Fig. 7A). In addition, the authors showed that the presence of electron withdrawing group at m- and p-position of phenyl ring B is required for the inhibition. The geometries were obtained by modeling the minimum energy. The vasoactive hormone angiotensin II produced by the reninangiotensin system (RAS) plays an important role in the cardiovascular disease therapy because it is involved in the regulation of fluid volume, electrolyte balance and blood volume in mammals [193, 194]. Zhou et al. [195] carried out a QSAR study for angiotensin II antagonists using robust boosting partial least squares (RBPLS) regression and a series of 4H-1,2,4-triazoles (Fig. 7B). RBPLS works by sequentially employing PLS method to the robustly reweighed versions of the training compounds, and then combing these resulting predictors through weighed median. The results obtained by RBPLS have been compared to those obtained by boosting partial least squares (BPLS) regression and partial least squares (PLS) regression, showing the good performance of RBPLS in improving the QSAR modeling. In addition, the study demonstrates that the interaction of angiotensin II antagonists is complex and includes topological, spatial, thermodynamic and electronic effects.

role of 5-HT2A receptors in mediating the hypotensive action of title compounds. The hypotensive activity was the dependent variable and different physicochemical parameters as independent parameters (hydrophobicity, electronic effect, resonance effect, field effect and steric effect) for the substituents of the aromatic rings. The best QSAR model was characterized by r = 0.846. The T-type or low voltage activated (LVA) calcium channels are involved in the neuronal excitability and in the control of blood pressure [183]. Therefore, they are important therapeutic targets for the treatment of epilepsy, neuropathic pain, and cardiovascular diseases such as hypertension and angina pectoris [184]. Mibefradil (Fig. 6A) is a T-type calcium channel blocker that has been used in the treatment of hypertension and stable angina [185] but it was withdrawn from the US market in 1998 due to the potential harmful interactions with other drugs [186]. KYS05090 (Fig. 6B) is another very potent blocker against T-type calcium channel, compared with doxorubicin, effective against some human cancer cells and without acute toxicity [187]. Jeong et al. [188] carried out a CoMSIA study based on 42 3,4-dihydroquinazolines (Fig. 6C) in order to find out the pharmacophore elements for T-type calcium channel blocking activity. KYS05090 was used to align the molecules. The resulted 3D-QSAR model (q2 = 0.642, r2 = 0.874, training set; r2pred = 0.884, test set) can be used as a guide to the design of new chemical entities with high potency. The steric, electrostatic, and hydrophobic potential fields for CoMSIA were calculated at each lattice intersection of a regularly spaced grid of 2.0 Å and an attenuation factor of 0.3. The regression analysis of the CoMSIA field energies was performed using PLS with LOO (leave-one-out) cross-validation. The results show that the CoMSIA hydrophobic descriptor played a more significant role (46.8% of contribution) than descriptors such as steric (28.3%) and electrostatic (24.9%) in the prediction of biological activity. In addition, they have shown the importance of the presence of bulky groups at C-2, C-3, and C-4 positions in 3,4dihydroquinazoline skeleton for the channel-blocking activity. Potassium (K+) channel family contains voltage dependent, 2+ Ca activated, receptor operated, ATP sensitive, Na+ activated and cell volume sensitive channels. The ATP-sensitive K+ (KATP) channels are distributed in a wide variety of tissues like skeletal and smooth muscle cells, cardiac myocytes and neurons. KATP channels are activated by drugs like minoxidil, diazoxide, pinacidil and benzopyran derivatives e.g., cromakalim, bimakalim, etc. [189]. Potassium (K+) channel openers (PCOs) are used for the treatment

A

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Fig. (6). T-type calcium channel blockers: mibefradil (A) and KYS05090 (B); structures of the studied 3,4-dihydroquinazolines (C).

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Current Pharmaceutical Design, 2010, Vol. 16, No. 24

A

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Fig. (7). Structure of R/S-3,4-dihydro-2,2-dimethyl-6-halo-4-(substituted phenylaminocarbonylamino)-2H-1-benzopyrans as pancreatic -cell KATP channel openers (A) and 4H-1,2,4-triazoles as angiotensin II antagonists (B).

Leukotrienes have been shown to be involved in a variety of diseases such as cardiovascular diseases, cancer, asthma, ulcerative colitis, and rhinitis [196-198]. Leukotriene A4 (LTA4) can then be exported from the cell to the transcellular metabolism or converted either to pro-inflammatory LTB4 or to bronchoconstrictive, vasoconstrictive, and pro-inflammatory cysteinyl leukotrienes (CysLTs), namely LTC4, LTD4, and LTE4. 5-Lipoxy-genase-Activating Protein (FLAP) is a key enzyme of leukotriene synthesis and, therefore, the cellular leukotriene synthesis can be completely inhibited by compounds that bind to FLAP [199]. Ma et al. [200] carried out comparative CoMFA and molecular docking studies based on a series of 31 substituted 2,2-bisaryl-bicycloheptanes FLAP inhibitors. Based on the docking conformations, highly predictive CoMFA model was performed using the predicted binding free energies (ChemScore), with a leave-one-out cross-validated q2 of 0.651. The non-cross-validated analysis with four optimum components revealed r2 value of 0.972, F=175.674, and an estimated standard error of 0.169. The predictive ability of this model was validated by the testing set with r2 value of 0.920. The analyses may be used to design more potent FLAP inhibitors and predict their activities prior to synthesis. The anti-arrhythmic activity of phenylpyridines has been studied by Hasegawa et al. [201] with the partial least squares (PLS) method. The model based on three physicochemical parameters successfully separated active from inactive compounds. The results show that it is possible to obtain a more potent anti-arrhythmic agent by increasing the molar refractivity value of a substituent on the benzene ring, increasing the torsion angle between amide function and the benzene ring and decreasing the proton affinity of an amino group of a phenylpyridine. Cardiotoxicity represents a high priority for both regulatory safety assessment, and a high liability for the pharmaceutical industry product revenue. Thus, there are several drugs that have been removed from the market due to cardiac adverse effects (AEs) and they contain: astemizole, chlorphentermine, cisapride, cloforex, dexfenfluramine, dithiazanine iodide, encainide, fenfluramine, grepafloxacin, prenylamine, and terfenadine [202]. There are many different causes for drug-related toxicity, but two mechanisms are the most studied and best understood. The first cause is linked to the activity of the heart muscle that forces the blood to flow throughout the body. Unlike the liver, the heart has a reduced capacity to store the energy and the chemicals that interfere with the production of energy substrates through glycolysis, or the generation of adenosine triphosphate through the Krebs cycle, can cause toxicity. Some examples of these drugs are cyanide, emetine, and doxorubicin [203]. The second cause is related to substances that interfere with the cardiac Purkinje nerve fibers that control the rhythmic function of the heart and cause fatal arrhythmias, prolong the QT interval, and/or decrease re-uptake of norepinephrine after release from noradrenergic cardiac neurons [204].

Recently, Matthews and Frid [205] carried out a report that describes the compilation of a database of drug-related cardiac AEs that was used to construct QSAR models to predict these AEs, in order to identify properties of pharmaceuticals correlated with the AEs, and to identify plausible mechanisms of action (MOAs) causing the AEs. This database of 396,985 cardiac AE reports was linked to 1632 approved drugs and their chemical structures, 1851 clinical indications (CIs), 997 therapeutic targets (TTs), 432 pharmacological MOAs, and 21,180 affinity coefficients (ACs) for the MOA receptors. AEs were obtained from the Food and Drug Administration’s (FDA’s) Spontaneous Reporting System (SRS) and Adverse Event Reporting System (AERS) and publicly available medical literature. Drug TTs were obtained from Integrity™ (http://www.thomson-reuters.com/); drug MOAs and ACs were predicted by BioEpisteme™ (http://www.prousre-search.com/ Epistemic/BioEpisteme. aspx). Significant cardiac AEs and patient exposures were estimated according to the proportional reporting ratios (PRRs) for each drug and each AE endpoint as a percentage of the total AEs. Cardiac AE endpoints were bundled according to toxicological mechanisms and concordance of drug-related findings. The results revealed that significant cardiac AEs formed 9 clusters affecting Purkinje nerve fibers (arrhythmia, bradycardia, conduction disorder, electrocardiogram, palpitations, QT prolongation, rate rhythm composite, tachycardia, and Torsades de pointes), and 5 clusters affecting the heart muscle (coronary artery disorders, heart failure, myocardial disorders, myocardial infarction, and valve disorders). Based on the observation that each drug had one TT and up to 9 off-target MOAs, cardiac AEs were highly correlated with drugs affecting cardiovascular and cardioneurological functions and certain MOAs (e.g., alpha- and beta-adeno, dopamine, and hydroxytryptomine receptors). QSAR in Oncology Breast, prostate, lung and colorectal cancers are the most frequent types of cancer [206-209] in the world. Cancer remains the second most common cause of death after heart-related problems and it will become the main cause of death in developed countries during this century even if there has been an increase in the survival rate of cancer patients. Free radicals play important roles in the physiology and pathology of humans [210] due to their capacity to damage the membranes, proteins, enzymes or DNA [211]. Thus, the increased concentration of the free radicals is enhancing the risk of diseases such as cancer, Alzheimer’s, Parkinson’s [212] or angiocardiopathy [213]. Between the synthetic molecules, diarylamines have been found to present antioxidant activity [214]. Several previous Structure-Activity Relationship (SAR) studies have been performed on the antioxidant activity of diaryl and di-heter-oarylamines derivatives of benzo[b]thio-phenes [215, 216] and it has been shown that the antioxidant activity of these molecules was related to the position of arylamination and the number and position of substitution

2648 Current Pharmaceutical Design, 2010, Vol. 16, No. 24

groups on both benzene or thiophene rings. Abreu et al. [217] studied the first QSAR model for these compounds in order to evaluate the antioxidant activity, specifically the radical scavenger activity (RSA), of 26 di(hetero)arylamines’ derivatives of benzo[b]thiophenes. The partial least squares projection of latent structures (PLS) method [218] has been used to build this model based on four molecular descriptors, belonging to RDF (Radial Distribution Function) descriptors (RDF020e and RDF045e) and 2D-autocorrelation descriptors (GATS8p and MATS5e). RDF descriptors quantify the presence of electronegative atoms at the inner atmosphere of the com-pounds to increase RSA and the 2Dautocorrelation descriptors associate the presence of polarizable and electronegative pairs of atoms, at specific topological distance, with the RSA of the compounds. The GTP-binding protein Ras play a central role in cell signaling pathways that govern cell growth and its mutations can lead to cellular transformation and uncontrolled proliferation [219]. The mutant Ras genes are often found in human tumors and make the Ras protein a target in the anticancer therapy [220, 221]. Puntambekar et al. [222] proposed a 3D-QSAR study using three different chemical series reported as selective farnesyltransferase (FTase) inhibitors employing CoMFA and CoMSIA techniques. The molecules were: 3-aminopyrrolidinone derivatives (training set N = 38, test set N = 7), 2-amino-nicotinonitriles (training set N = 46, test set N = 13) and 1-aryl-1-imidazolyl methyl ethers (training set N = 35, test set N = 5). The use of the steric and electrostatic fields leads to the fact that CoMFA yielded relatively improved models for 3aminopyrrolidinones and 1-aryl-1-imidazolyl methyl ethers. CoMSIA models were statistically significant for 2-amino-nicotinonitriles indicating the importance of hydrophobic, H-bond donor and acceptor for the FTase inhibitory activity. Gonzáles-Díaz et al. [223] used MARCH-INSIDE to discover anticancer compounds using QSAR computer-aided molecular design by introducing a simple stochastic approach that models the movement of electrons throughout chemical bonds (see Fig. 8A). They used the Markov matrix to code the structural information in QSAR indices. Markov's chains were first used at the beginning of the last century [224]. Up to now, Markov’s chains (MCH) theory is increasingly used in several fields such as Artificial Intelligence [225], Epidemiology [226], and Medicine [227]. Therefore, this model describes the probabilities (kpij) with which electrons move from any arbitrary atom ai at time t0 (in black) to other aj atoms (in white) during discrete time periods tk (k=1, 2, 3, ...) and throughout the chemical bonds. The external electron layers of any atom core in the molecule (valence shell) have been considered as states of the MCH. The matrix 1P contains elements of 1pij, it is called the 1-step electron-transition stochastic matrix and it is a square table of order n, the number of atoms in the molecule (Fig. 8B-C). The self-return probabilities of this matrix throughout time (SRk) have been used as molecular descriptors. In the first step, SRk has been calculated for a large series of anticancer and non-anticancer chemicals. In the second step, the use of the k-Means Cluster Analysis [228] allows them to split the data series into clusters and ensure a representative design of training and predicting series. Next, the classification function has been developed using Linear Discriminant Analysis (LDA) [229]. The obtained QSAR model can discriminate between anticancer compounds and non-active compounds with a correct global classification of 90.5% in the training series (86.07% of the compounds in the predicting series) and has been used to perform a virtual screening of a combinatorial library of coumarins. Thus, the biological assay of some furocoumarins, selected by virtual screening using this QSAR model, gives good results. In particular, a tetracyclic derivative of 5-methoxy-psoralen (5-MOP) has an IC50 against HL-60 tumoral line about 6 to 10 times lower than those for 8-MOP and 5-MOP (reference drugs), respectively.

Munteanu et al.

Fig. (8). Diagrammatic representation of random electron distribution in a Markovian model (tstationary is the stationary time, the time at which electrons reach equilibrium distribution around atoms) (A); Definition (B) and calculation (C) of the 1 matrix for a specific case.

Molecular chaperones are another important component involved in apoptosis and oncogenesis. Chaperones are responsible for maintaining the appropriate folding and 3D conformation of proteins in the cell and control the balance between the synthesis and degradation of many proteins [230]. Heat shock protein 90 (HSP90) is an ATP-dependent molecular chaperone which is an exciting new target for the development of innovative chemogenomics approaches [231, 232]. Human HSP90 family includes 17 genes [233] and their expression is associated with many types of tumors including breast cancer, pancreatic carcinomia, human leukemia and systemic lupus ery-thematosus, as well as multidrug resistance. Inhibition of HSP90 leads to deregulation of the following pathways: self-sufficiency in growth signals, tissues invasion/metastasis, insensitivity to antigrowth signals, sustained angiogenesis, evasion of apoptosis and limitless replicative potential (responsible for the cancer cell survival [234]). Therefore, the

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The ABCG2 transporter was discovered simultaneously in three different laboratories and was respectively called ABCP for its abundance in placenta [241], BCRP for its identification from resistant breast cancer cells [242] and MXR for its ability to confer cell-growth resistance to mitoxantrone [243]. ABCG2 is able to transport many types of drug substrates [244, 245]. First of all, many anticancer chemotherapeutics are efficiently effluxed, such as mitoxantrone [243], camptothecins including topotecan [246], irinotecan [247] and its SN-38 metabolite [248], antifolates such as methotrexate [249], epipodophyllotoxins such as etoposide and teniposide [250], tyrosine kinase inhibitors such as Imatinib [251], Gefitinib [252] and possibly Nilotinib [253], other kinase inhibitors such as flavopiridol [254], and indolocarbazoles [255]. Since the early discovery of ABCB1 as a major player in cancer cells resistance to chemotherapy as an efflux pump, many efforts have been made, on the one hand, to understand the structural requirements of the transported substrates and, on the other hand, to find efficient inhibitors able to abolish the efflux activity of the transporter and then to chemosensitize the proliferation of cancer cells. Nicolle et al. [256] carried out a QSAR study to find inhibitors for ABCG2 as future candidates for clinical trials. A special attention was drawn on flavonoids (Fig. 9) which constitute a structurally-diverse class of compounds, well-suited to identify potent ABCG2-specific inhibitors. The epigallocatechin gallate (EGCG) molecule from the green tea polyphenols was demonstrated to be one of the most potent chemopreventive agents that can induce apoptosis, suppress the formation and growth of human cancers including colorectal cancer (CRC) [257]. The treatment of colon cancers by EGCG has resulted in a strong activation of AMP-activated protein kinase (AMPK) and an inhibition of COX-2 expression. The classical treatment of CRC with 5-fluorouracil and leucovorin has been replaced by the combination chemotherapy at the stage III of the disease. Therefore, the oxaliplatin-based chemotherapy is now considered as the standard care in node positive colon cancer but still controversial for the patients with node-negative disease. Thus, fluoropyrimidines become an alternative to 5-fluorouracil. Despite the progress achieved with the introduction of new cytotoxic agents, recurrence rates for the colon cancer patients with respect to stage II/III of the disease remain >20% [258]. Taxanes are considered to be the most powerful group of compounds among the current novel chemotherapeutic drugs. Taxane analogues such as paclitaxel and

inhibition of HSP90 will be an important pharmacological platform for anticancer therapy [233, 235]. Sakkiah et al. [236] carried out a 3D QSAR pharmacophore based on virtual screening and molecular docking for identification of potential HSP90 inhibitors using a set of 16 compounds with diverse scaffold. The training set includes two natural potent inhibitors of HSP90, Radicicol and Geldanamycin. 3D QSAR Pharmacophore Generation module/Discovery Studio (DS) was used to construct a pharmacophore model using hydrogen bond acceptor, hydrogen bond donor, hydrophobic and ring aromatic chemical features. The best five features pf the pharmacophore model include two hydrogen bond acceptors, three hydrophobic features, which have the highest correlation coefficient (0.93), cost difference (73.88), low RMS (1.24); in addition, it shows a high goodness of fit and enrichment factor. The model was used as a 3D query for virtual screening to retrieve potential inhibitors from Maybridge and Scaffold databases, which consist of 60,000 and 100,677 compounds respectively. The hit compounds were subsequently subjected to molecular docking studies and finally, 36 compounds were obtained according to the consensus scoring function. Transforming growth factor- (TGF-) is a cytokine that becomes a new target due to its involvement in a number of diseases such as inflammation, fibrosis, cancer, asthma and cardiovascular diseases by mediating pathways involving the regulation of gene response and DNA transcription factors. TGF- is dependent upon the activation of type I (TGF- RI) and type II (TGF- RII) receptors [237] and, therefore, the inhibition of the TGF-b RI may be useful for the treatment of a number of diseases such as fibrosis and cancer [238, 239]. Yang et al. [240] proposed a docking study and 3D-QSAR analyses of TGF- RI Inhibitors. The genetic algorithm search method in the docking program GOLD 3.0.1 was employed to determine the likely binding mode conformations of 70 inhibitors in the active site of TGF- RI. The best CoMFA model was characterized by q2 of 0.589 and r2 of 0.932 and was validated with a number of compounds that were not included in the original training set. In addition, the model was mapped back to the binding site of the TGF- RI in order to study the interactions between the inhibitors and TGF- RI. The robustness, predictive ability and automated alignment generation of this model make it a potential tool for the design and development of new drug leading to the inhibition of TGF- RI.

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drugs. Taxane analogues such as paclitaxel and docetaxel are already in use for the treatment of various types of cancers, including breast, colon, lung, ovarian, and prostate [259-261]. These two drugs bind to the b-subunit of tubulin and promote tubulin polymerization, leading to the inhibition of microtubule dynamics, cell cycle arrest, and ultimately cell death by apoptosis [262, 263]. Verma et al. [264] studied the taxane analogues (Fig. 10) against CRC using QSAR methods. Thus, four series of taxane derivatives were used to correlate their inhibitory activities against CRCs mainly with the hydrophobic and steric descriptors of their substituents in order to gain a better understanding of their chemicalbiological interactions. The models have r2 values ranging between 0.807 and 0.855. The results showed that the steric and hydrophobic parameters of the substituents are the two most important determinants for the activities of taxane analogues against CRC, with a major contribution coming from the molar refractivity of the substituents. O

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ACKNOWLEDGMENTS Munteanu C. R. and José A. Seoane acknowledge the funding support for a research position by “Isidro Parga Pondal” Program and an “Isabel Barreto” grant from Xunta de Galicia (Spain), respectively. This work is supported by the following projects: “Galician Network for Colorectal Cancer Research” (REGICC, Ref. 2009/58) from the General Directorate of Research, Development and Innovation of Xunta de Galicia, “Ibero-American Network of the Nano-Bio-Info-Cogno Convergent Technologies”, Ibero-NBIC Network (209RT-0366) funded by CYTED (Spain), grant Ref. PIO52048 and RD07/0067/0005 funded by the Carlos III Health Institute and “PHR2.0: Registro Personal de Salud en Web 2.0” (Ref. TSI-020110-2009-53) funded by the Spanish Ministry of Industry, Tourism and Trade. REFERENCES [1] [2]

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Fig. (10). Taxane analogues against CRC.

CONCLUSIONS The work reviewed the most important QSAR results on drug discovery and design for complex diseases in three fields such as Neurology, Cardiology and Oncology. The fast QSAR methods allow to obtain mathematical models as tools for new drug design against important diseases by using molecular descriptors based on molecular properties and complex network/graph theory.

[20]

[21] [22]

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[29]

[30] [31] [32]

[33]

[34] [35]

[36] [37] [38] [39]

[40]

[41] [42]

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Received: May 31, 2010

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