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Quantitative structure–activity relationship (QSAR) studies as strategic approach in drug discovery

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Abstract

Drug design is a process which is driven by technological breakthroughs implying advanced experimental and computational methods. Nowadays, the techniques or the drug design methods are of paramount importance for prediction of biological profile, identification of hits, generation of leads, and moreover to accelerate the optimization of leads into drug candidates. Quantitative structure–activity relationship (QSAR) has served as a valuable predictive tool in the design of pharmaceuticals and agrochemicals. From decades to recent research, QSAR methods have been applied in the development of relationship between properties of chemical substances and their biological activities to obtain a reliable statistical model for prediction of the activities of new chemical entities. Classical QSAR studies include ligands with their binding sites, inhibition constants, rate constants, and other biological end points, in addition molecular to properties such as lipophilicity, polarizability, electronic, and steric properties or with certain structural features. 3D-QSAR has emerged as a natural extension to the classical Hansch and Free–Wilson approaches, which exploit the three-dimensional properties of the ligands to predict their biological activities using robust chemometric techniques such as PLS, G/PLS, and ANN. This paper provides an overview of 1-6 dimension-based developed QSAR methods and their approaches. In particular, we present various dimensional QSAR approaches, such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis, Topomer CoMFA, self-organizing molecular field analysis, comparative molecule/pseudo receptor interaction analysis, comparative molecular active site analysis, and FLUFF-BALL, 4D-QSAR, and G-QSAR approaches.

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References

  • Ajmani S, Jadhav K, Kulkarni SA (2006) Three-dimensional QSAR using the k-nearest neighbor method and its interpretation. J Chem Inf Model 46:24–31

    Article  CAS  PubMed  Google Scholar 

  • Akamatsu M (2002) Current state and perspectives of 3D-QSAR. Curr Top Med Chem 2:1381–1394

    Article  CAS  PubMed  Google Scholar 

  • Albert A, Goldacre R, Phillips J (1948) The strength of heterocyclic bases. J Chem Soc 2:2240–2249

    Article  Google Scholar 

  • Albuquerque MG, Hopfinger AJ, Barreiro EJ, De-Alencastro RB (1998) Four-dimensional quantitative structure-activity relationship analysis of a series of interphenylene 7-oxabicycloheptane oxazole thromboxane A2 receptor antagonists. J Chem Inf Comput Sci 38:925–938

    Article  CAS  PubMed  Google Scholar 

  • Albuquerque M, Brito M, Cunha E, Alencastro R, Antunes O, Castro H, Rodrigues C (2007) Multidimensional-QSAR: beyond the third-dimension in drug design. Curr Methods Med Chem Biol Phys 1:91–100

    CAS  Google Scholar 

  • Aldenderfer MS, Blashfield RK (1984) A review of clustering methods. In: Aldenderfer MS, Blashfield RK (eds) Cluster analysis. SAGE Publications Ltd, London, pp 33–61

    Google Scholar 

  • Andrade CH, Pasqualoto KFM, Ferreira EI, Hopfinger AJ (2009) Rational design and 3D-pharmacophore mapping of 5′-thiourea-substituted alpha-thymidine analogues as mycobacterial TMPK inhibitors. J Chem Inf Model 49:1070–1078

    Article  CAS  PubMed  Google Scholar 

  • Andricopulo AD, Salum LB, Abraham DJ (2009) Structure-based drug design strategies in medicinal chemistry. Curr Top Med Chem 9:771–790

    Article  CAS  PubMed  Google Scholar 

  • Baskin II, Palyulin VA, Zefirov NS (2008) Neural networks in building QSAR models. Methods Mol Biol 458:137–158

    PubMed  Google Scholar 

  • Baxter AD, Lockey PM (2001) ‘Hit’ to ‘lead’ and ‘lead’ to ‘candidate’ optimization using multi-parametric principles. Drug Discov World 2:9–15

    Google Scholar 

  • Berk RA (2003a) Simple linear regression. Regression analysis: a constructive critique. SAGE Publications Ltd, London, pp 21–38

    Google Scholar 

  • Berk RA (2003b) Some popular extensions of multiple regression. Regression analysis: a constructive critique. SAGE Publications Ltd, London, pp 125–150

    Google Scholar 

  • Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Boyd MR, Paull KD (1995) Some practical considerations and applications of the National Cancer Institute in vitro anticancer drug discovery screen. Drug devel Res 34:91–109

    Article  CAS  Google Scholar 

  • Breu B, Silber K, Gohlke H (2007) Consensus adaptation of fields for molecular comparison (AFMoC) models incorporate ligand and receptor conformational variability into tailor-made scoring functions. J Chem Inf Model 47:2383–2400

    Article  CAS  PubMed  Google Scholar 

  • Bruneau P, McElroy NR (2006) logD 7.4 modeling using Bayesian regularized neural networks. Assessment and correction of the errors of prediction. J Chem Inf Model 46:1379–1387

    Article  CAS  PubMed  Google Scholar 

  • Collins FS, Patrinos A, Jordan E, Chakravati A, Gesteland R, Walters L (1998) New goals for the US human genome project: 1998–2003. Science 282:682–689

    Article  CAS  PubMed  Google Scholar 

  • Colosi LM, Huang Q, Weber WJ Jr (2010) QSAR-assisted design of an environmental catalyst for enhanced estrogen remediation. Chemosphere 81:897–903

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Datar PA, Khedkar SA, Malde AK, Coutinho EC (2006) Comparative residue interaction analysis (CoRIA): a 3D-QSAR approach to explore the binding contributions of active site residues with ligands. J Comput Aided Mol Des 20:343–360

    Article  CAS  PubMed  Google Scholar 

  • Dhaked DK, Verma J, Saran A, Coutinho EC (2009) Exploring the binding of HIV-1 integrase inhibitors by comparative residue interaction analysis (CoRIA). J Mol Model 15:233–245

    Article  CAS  PubMed  Google Scholar 

  • Dunn WJ III, Rogers D (1996) Genetic partial least squares in QSAR. In: Devillers J (ed) Genetic algorithms in molecular modeling. Academic Press, London, pp 109–130

    Chapter  Google Scholar 

  • Dunteman GH (1989a) Basic concepts of principal components analysis. In: Dunteman GH (ed) Principal components analysis. SAGE Publications Ltd, London, pp 15–22

    Google Scholar 

  • Dunteman GH (1989b) Uses of principal components in regression analysis. In: Dunteman GH (ed) Principal components analysis. SAGE Publications Ltd., London, pp 65–74

    Google Scholar 

  • Fells JI, Tsukahara R, Liu J, Tigyi G, Parrill AL (2010) 2D binary QSAR modeling of LPA3 receptor antagonism. J Mol Graph Model 28:828–833

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Free SM Jr, Wilson JW (1964) A mathematical contribution to structure–activity studies. J Med Chem 7:395–399

    Article  CAS  PubMed  Google Scholar 

  • Funar-Timofei S, Fabian WMF, Kurunczi L, Goodarzi M, Ali ST, Heyden YV (2012) Modelling heterocyclic azo dye affinities for cellulose fibres by computational approaches. Dyes Pigm 94:278–289

    Article  CAS  Google Scholar 

  • Gieleciak R, Polanski J (2007) Modeling robust QSAR. 2. Iterative variable elimination schemes for CoMSA: application for modeling benzoic acid pKa values. J Chem Inf Model 47:547–556

    Article  CAS  PubMed  Google Scholar 

  • Guido RVC, Oliva G, Andricopulo AD (2008) Virtual screening and its integration with modern drug design technologies. Curr Med Chem 15:37–46

    Article  CAS  PubMed  Google Scholar 

  • Hahn M (1995) Receptor surface models. 1. Definition and construction. J Med Chem 38:2080–2090

    Article  CAS  PubMed  Google Scholar 

  • Hammett LP (1935) Some relations between reaction rates and equilibrium constants. Chem Rev 17:125–136

    Article  CAS  Google Scholar 

  • Hammett LP (1937) The effect of structure upon the reactions of organic compounds benzene derivatives. J Am Chem Soc 59:96–103

    Article  CAS  Google Scholar 

  • Hansch C (1969) Quantitative approach to biochemical structure-activity relationships. Acc Chem Res 2:232–239

    Article  CAS  Google Scholar 

  • Hansch C, Fujita T (1964) p-σ-π Analysis. A method for the correlation of biological activity and chemical structure. J Am Chem Soc 86:1616–1626

    Article  CAS  Google Scholar 

  • Hansch C, Gao H (1997) Comparative QSAR: radical reactions of benzene derivatives in chemistry and biology. Chem Rev 97:2995–3060

    Article  CAS  PubMed  Google Scholar 

  • Hopfinger A (2001) 4D-QSAR package user’s manual 3.0. The Chem21 Group Inc, Lake Forest

    Google Scholar 

  • Hopfinger AJ, Tokarski JS (1997) Three-dimensional quantitative structure-activity relationship analysis. In: Charifson P (ed) Practical application of computer-aided drug design. Marcel Dekker, New York, pp 105–164

    Google Scholar 

  • Hopfinger A, Wang S, Tokarski J, Jin B, Albuquerque M, Madhav P, Duraiswami C (1997) Construction of 3D-QSAR models using the 4D-QSAR analysis formalism. J Am Chem Soc 119:10509–10524

    Article  CAS  Google Scholar 

  • In Y, Chai HH, No KT (2005) A partition coefficient calculation method with the SFED model. J Chem Inf Model 45:254–263

    Article  CAS  PubMed  Google Scholar 

  • Ivanciuc O, Ivanciuc T, Cabrol-Bass D (2000) 3D quantitative structure activity relationships with CoRSA. Comparative receptor surface analysis. Application to calcium channel agonists. Analysis 28:637–642

    Article  CAS  Google Scholar 

  • Jojart B, Martinek TA, Marki A (2005) The 3D structure of the binding pocket of the human oxytocin receptor for benzoxazine antagonists, determined by molecular docking, scoring functions and 3D-QSAR methods. J Comput Aided Mol Des 19:341–356

    Article  CAS  PubMed  Google Scholar 

  • Jorgensen WL, Duffy EM (2002) Prediction of drug solubility from structure. Adv Drug Deliv Rev 54:355–366

    Article  CAS  PubMed  Google Scholar 

  • Katritzky AR, Lobanov VS, Karelson M (1995) QSPR: the correlation and quantitative prediction of chemical and physical properties from structure. Chem Soc Rev 24:279–287

    Article  CAS  Google Scholar 

  • Kennedy T (1997) Managing the drug discovery/development interface. Drug Discov Today 2:436–444

    Article  Google Scholar 

  • Khedkar SA, Malde AK, Coutinho EC (2007) Design of inhibitors of the MurF enzyme of Streptococcus pneumoniae using docking, 3DQSAR, and de novo design. J Chem Inf Model 47:1839–1846

    Article  CAS  PubMed  Google Scholar 

  • Kim KH (1995) Comparative molecular field analysis (CoMFA). In: Dean PM (ed) Molecular similarity in drug design. Blackie academic and professional, Glasgow, pp 291–331

    Chapter  Google Scholar 

  • Koo HM, Monks A, Mikheev A, Rubinstein LV, Gray-Goodrich M, McWilliams MJ, Alvord WG, Oie HK, Gazdar AF, Paull KD, Zarbl H, Vande-Woude GF (1996) Enhanced sensitivity to 1-beta-D-arabinofuranosylcytosine and topoisomerase II inhibitors in tumor cell lines harboring activated RAS oncogenes. Cancer Res 56:5211–5216

    CAS  PubMed  Google Scholar 

  • Korhonen SP, Tuppurainen K, Laatikainen R, Perakyla M (2003) FLUFF-BALL, a template-based grid-independent superposition and QSAR technique: validation using a benchmark steroid data set. J Chem Inf Comput Sci 43:1780–1793

    Article  CAS  PubMed  Google Scholar 

  • Kotani T, Higashiura K (2004) Comparative molecular active site analysis (CoMASA). 1. An approach to rapid evaluation of 3D QSAR. J Med Chem 47:2732–2742

    Article  CAS  PubMed  Google Scholar 

  • Krasowski MD, Hong X, Hopfinger AJ, Harrison NL (2002) 4D-QSAR analysis of a set of propofol analogues: mapping binding sites for an anesthetic phenol on the GABA (A) receptor. J Med Chem 45:3210–3221

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Kubinyi H (1976) Quantitative structure-activity relationships. IV. Non-linear dependence of biological activity on hydrophobic character: a new model. Arzneimittelforschung 26:1991–1997

    CAS  PubMed  Google Scholar 

  • Kubinyi H (2004) 2D QSAR models: Hansch and Free-Wilson analyses. In: Bultinck P, Winter HD, Langenaeker W, Tollenaere JP (eds) Computational medicinal chemistry for drug discovery. Marcel Dekker, New York, pp 539–570

    Google Scholar 

  • Labute P (1999) Binary QSAR: a new method for the determination of quantitative structure activity relationships. Pac Symp Biocomput 1999:444–455

    Google Scholar 

  • Lee PH, Ayyampalayam SN, Carreira LA, Shalaeva M, Bhattachar S, Coselmon R, Poole S, Gifford E, Lombardo F (2007) In silico prediction of ionization constants of drugs. Mol Pharm 4:498–512

    Article  CAS  PubMed  Google Scholar 

  • Lee AC, Yu JY, Crippen GM (2008) pKa prediction of monoprotic small molecules the SMARTS way. J Chem Inf Model 48:2042–2053

    Article  CAS  PubMed  Google Scholar 

  • Liu HY, Liu SS, Qin LT, Mo LY (2009) CoMFA and CoMSIA analysis of 2,4-thiazolidinediones derivatives as aldose reductase inhibitors. J Mol Model 15:837–845

    Article  CAS  PubMed  Google Scholar 

  • Liu J, Li Y, Zhang S, Xiao Z, Ai C (2011a) Studies of new fused benzazepine as selective dopamine D3 receptor antagonists using 3D-QSAR, molecular docking and molecular dynamics. Int J Mol Sci 12:1196–1221

    Article  PubMed Central  PubMed  Google Scholar 

  • Liu J, Zhang H, Xiao Z, Wang F, Wang X, Wang Y (2011b) Combined 3D-QSAR, molecular docking and molecular dynamics study on derivatives of peptide epoxyketone and tyropeptin-boronic acid as inhibitors against the β5 subunit of human 20S proteasome. Int J Mol Sci 12:1807–1835

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Lombardino JG, Lowe JA (2004) The role of the medicinal chemist in drug discovery-then and now. Nat Rev Drug Discov 3:853–862

    Article  CAS  PubMed  Google Scholar 

  • Lowe EW Jr, Ferrebee A, Rodriguez AL, Jeffrey-Connc P, Meiler J (2010) 3D-QSAR CoMFA study of benzoxazepine derivatives as mGluR5 positive allosteric modulators. Bioorg Med Chem Lett 20:5922–5924

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Luo X, Shu S, Wang Y, Liu J, Yang W, Lin Z (2012) 3D-QSAR studies of dihydropyrazole and dihydropyrrole derivatives as inhibitors of human mitotic kinesin Eg5 based on molecular docking. Molecules 17:2015–2029

    Article  CAS  PubMed  Google Scholar 

  • Magdziarz T, Lozowicka B, Gieleciak R, Bak A, Polanski J, Chilmonczyk Z (2006) 3D QSAR study of hypolipidemic asarones by comparative molecular surface analysis. Bioorg Med Chem 14:1630–1643

    Article  CAS  PubMed  Google Scholar 

  • Magdziarz T, Mazur P, Polanski J (2009) Receptor independent and receptor dependent CoMSA modeling with IVE-PLS: application to CBG benchmark steroids and reductase activators. J Mol Model 15:41–51

    Article  CAS  PubMed  Google Scholar 

  • Manly CJ, Louise-May S, Hammer JD (2001) The impact of informatics and computational chemistry on synthesis and screening. Drug Discov Today 6:1101–1110

    Article  CAS  PubMed  Google Scholar 

  • Matyus P, Borosy AP (1998) Three dimensional structure-activity relationships. Acta Pharm Hung 68:33–38

    CAS  PubMed  Google Scholar 

  • Mills EJ (1884) On melting point and boiling point as related to composition. Philos Mag 17:173–187

    Article  Google Scholar 

  • Muller G (2003) Medicinal chemistry of target family-directed masterkeys. Drug Discov Today 8:681–691

    Article  PubMed  Google Scholar 

  • Murugesan V, Prabhakar YS, Katti SB (2009) CoMFA and CoMSIA studies on thiazolidin-4-one as anti-HIV-1 agents. J Mol Graph Model 27:735–743

    Article  CAS  PubMed  Google Scholar 

  • Nakao K, Fujikawa M, Shimizu R, Akamatsu M (2009) QSAR application for the prediction of compound permeability with in silico descriptors in practical use. J Comput Aided Mol Des 23:309–319

    Article  CAS  PubMed  Google Scholar 

  • Natesan S, Wang T, Lukacova V, Bartus V, Khandelwal A, Subramaniam R, Balaz S (2012) Cellular quantitative structure-activity relationship (Cell-QSAR): conceptual dissection of receptor binding and intracellular disposition in antifilarial activities of selwood antimycins. J Med Chem 55:3699–3712

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Noolvi MN, Patel HM, Bhardwaj V (2010) 2D QSAR studies on a series of 4-anilino quinazoline derivatives as tyrosine kinase (EGFR) inhibitor: an approach to design anticancer agents. Dig J Nanomater Bios 5:387–401

    Google Scholar 

  • Noolvi MN, Patel HM, Bhardwaj V (2011) A comparative QSAR analysis of quinazoline analogues as tyrosine kinase (erbB-2) inhibitors. Med Chem 7:200–212

    Article  CAS  PubMed  Google Scholar 

  • Norinder U, Bergstrom CAS (2006) Prediction of ADMET properties. ChemMedChem 1:920–937

    Article  CAS  PubMed  Google Scholar 

  • Oprea TI (2004) 3D QSAR modeling in drug design. In: Bultinck P, Winter HD, Langenaeker W, Tollenaere JP (eds) Computational medicinal chemistry for drug discovery. Marcel Dekker, New York, pp 571–616

    Google Scholar 

  • Palmer DS, O’Boyle NM, Glen RC, Mitchell JBO (2007) Random forest models to predict aqueous solubility. J Chem Inf Model 47:150–158

    Article  CAS  PubMed  Google Scholar 

  • Paull KD, Shoemaker RH, Hodes L, Monks A, Scudiero DA, Rubinstein L, Plowman J, Boyd MR (1989) Display and analysis of patterns of differential activity of drugs against human tumor cell lines: development of mean graph and COMPARE algorithm. J Natl Cancer Inst 81:1088–1092

    Article  CAS  PubMed  Google Scholar 

  • Polanski J, Walczak B (2000) The comparative molecular surface analysis (COMSA): a novel tool for molecular design. Comput Chem 24:615–625

    Article  CAS  PubMed  Google Scholar 

  • Polanski J, Gieleciak R, Bak A (2002) The comparative molecular surface analysis (COMSA) a nongrid 3D QSAR method by a coupled neural network and PLS system: predicting pK(a) values of benzoic and alkanoic acids. J Chem Inf Comput Sci 42:184–191

    Article  CAS  PubMed  Google Scholar 

  • Robinson DD, Winn PJ, Lyne PD, Richards WG (1999) Self-organizing molecular field analysis: a tool for structure-activity studies. J Med Chem 42:573–583

    Article  CAS  PubMed  Google Scholar 

  • Rogers D, Hopfinger AJ (1994) Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J Chem Inf Comput Sci 34:854–866

    Article  CAS  Google Scholar 

  • Salum LB, Andricopulo AD (2009) Fragment-based QSAR: perspectives in drug design. Mol Divers 13:277–285

    Article  CAS  PubMed  Google Scholar 

  • Santos-Filho OA, Hopfinger AJ, Cherkasov A, De-Alencastro RB (2009) The receptor-dependent QSAR paradigm: an overview of the current state of the art. Med Chem 5:359–366

    Article  CAS  PubMed  Google Scholar 

  • Stewart L, Clark R, Behnke C (2002) High-throughput crystallization and structure determination in drug discovery. Drug Discov Today 7:187–196

    Article  CAS  PubMed  Google Scholar 

  • Taft RW (1952) Polar and steric substituent constants for aliphatic and o-benzoate groups from rates of esterification and hydrolysis of esters 1. J Am Chem Soc 74:3120–3128

    Article  CAS  Google Scholar 

  • Thipnate P, Liu J, Hannongbua S, Hopfinger AJ (2009) 3D pharmacophore mapping using 4D QSAR analysis for the cytotoxicity of lamellarins against human hormone-dependent T47D breast cancer cells. J Chem Inf Model 49:2312–2322

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Tian Y, Xu J, Li Z, Zhu Z, Zhang J, Wu S (2011) Combined 3D-QSAR and docking modelling study on indolocarbazole series compounds as Tie-2 inhibitors. Int J Mol Sci 12:5080–5097

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Ul-Haq Z, Wadood A, Uddin R (2009) CoMFA and CoMSIA 3D-QSAR analysis on hydroxamic acid derivatives as urease inhibitors. J Enzy Inhib Med Chem 24:272–278

    Article  CAS  Google Scholar 

  • Van Daele I, Munier-Lehmann H, Froeyen M, Balzarini J, Van Calenbergh S (2007) Rational design of 5′-thiourea-substituted alpha-thymidine analogues as thymidine monophosphate kinase inhibitors capable of inhibiting mycobacterial growth. J Med Chem 50:5281–5292

    Article  PubMed  CAS  Google Scholar 

  • Verma J, Khedkar VM, Prabhu AS, Khedkar SA, Malde AK, Coutinho EC (2008) A comprehensive analysis of the thermodynamic events involved in ligand-receptor binding using CoRIA and its variants. J Comput Aided Mol Des 22:91–104

    Article  CAS  PubMed  Google Scholar 

  • VLifeMDS 3.0 (2007) Molecular design suite developed by VLife Sciences Technologies Pvt Ltd. VLife Sciences Technologies Pvt Ltd., Pune

    Google Scholar 

  • Walczak B, Massart DL (2000) Local modeling with radial basis function networks. Chemom Intell Lab Syst 50:179–198

    Article  CAS  Google Scholar 

  • Walpole CS, Wrigglesworth R, Bevan S, Campbell EA, Dray A, James IF, Masdin KJ, Perkins MN, Winter J (1993) Analogues of capsaicin with agonist activity as novel analgesic agents; structure-activity studies 3. The hydrophobic side-chain “C-region”. J Med Chem 36:2381–2389

    Article  CAS  PubMed  Google Scholar 

  • Wang J, Krudy G, Hou T, Zhang W, Holland G, Xu X (2007) Development of reliable aqueous solubility models and their application in drug like analysis. J Chem Inf Model 47:1395–1404

    Article  CAS  PubMed  Google Scholar 

  • Weinstein JN, Myers T, Buolamwini J, Raghavan K, Van Osdol W, Licht J, Viswanadhan VN, Kohn KW, Rubinstein LV, Koutsoukos AD, Monks A, Scudiero DA, Anderson NL, Zaharevitz D, Chabner BA, Grever MR, Paull KD (1994) Predictive statistics and artificial intelligence in the US National Cancer Institute’s drug discovery program for cancer and AIDS. Stem Cells 12:13–22

    Article  CAS  PubMed  Google Scholar 

  • Wise M, Cramer RD, Smith D, Exman I (1983) Progress in three-dimensional drug design: the use of real time colour graphics and computer postulation of bioactive molecules in DYLOMMS. In: Dearden J (ed) Quantitative approaches to drug design. Elsevier, Amsterdam, pp 145–146

    Google Scholar 

  • Wold S, Johansson E, Cocchi M (1993) PLS: partial least squares projections to latent structures. In: Kubinyi H (ed) 3D QSAR in drug design: theory, methods and applications. ESCOM Science Publishers, Leiden, pp 523–550

    Google Scholar 

  • Yadav DK, Meena A, Srivastava A, Chanda D, Khan F, Chattopadhyay SK (2010) Development of QSAR model for immunomodulatory activity of natural coumarinolignoids. Drug Des Devel Ther 4:173–186

    PubMed Central  CAS  PubMed  Google Scholar 

  • Yang S, Chang S, Chen H, Chen CY (2011) Identification of potent EGFR inhibitors from TCM Database@Taiwan. PLoS Comput Biol 7:e1002189

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Zhang B, Li Y, Zhang H, Ai C (2010) 3D-QSAR and molecular docking studies on derivatives of MK-0457, GSK1070916 and SNS-314 as inhibitors against Aurora B Kinase. Int J Mol Sci 11:4326–4347

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Zhao H (2007) Scaffold selection and scaffold hopping in lead generation: a medicinal chemistry perspective. Drug Discov Today 12:149–155

    Article  CAS  PubMed  Google Scholar 

  • Zhao YH, Abraham MH, Ibrahim A, Fish PV, Cole S, Lewis ML, De Groot MJ, Reynolds DP (2007) Predicting penetration across the blood-brain barrier from simple descriptors and fragmentation schemes. J Chem Inf Model 47:170–175

    Article  CAS  PubMed  Google Scholar 

  • Zhou P, Tong J, Tian F, Li Z (2006) A novel comparative molecule/pseudo receptor interaction analysis. Chin Sci Bull 51:1824–1829

    Article  CAS  Google Scholar 

  • Zhu YQ, Lei M, Lu AJ, Zhao X, Yin XJ, Gao QZ (2009) 3D-QSAR studies of boron-containing dipeptides as proteasome inhibitors with CoMFA and CoMSIA methods. Eur J Med Chem 44:1486–1499

    Article  CAS  PubMed  Google Scholar 

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  1. Department of Pharmaceutical Chemistry, R.C. Patel Institute of Pharmaceutical Education and Research, Dhule, Shirpur, 425405, Maharashtra, India

    Harun M. Patel

  2. Department of Pharmaceutical Chemistry, Shree Dhanvantary Pharmacy College, Kim, Surat, 394110, Gujarat, India

    Malleshappa N. Noolvi

  3. Department of Biotechnology, Bioinformatics and Pharmacy, Jaypee University of Information Technology, Solan, Waknaghat, 173234, Himachal Pradesh, India

    Poonam Sharma, Sumit Bansal, Sandeep Lohan, Suthar Sharad Kumar, Vikrant Abbot, Saurabh Dhiman & Varun Bhardwaj

  4. Department of Bioinformatics, Shoolini University, Solan, Himachal Pradesh, India

    Varun Jaiswal

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Patel, H.M., Noolvi, M.N., Sharma, P. et al. Quantitative structure–activity relationship (QSAR) studies as strategic approach in drug discovery. Med Chem Res 23, 4991–5007 (2014). https://doi.org/10.1007/s00044-014-1072-3

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