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Chemoinformatics and QSAR

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Advances in Bioinformatics

Abstract

In recent years, constant increase in the performance of computer-based tools and several mathematical algorithms to solve chemistry-related problems. In recent years, screening of potent lead molecules using computational approaches has been gaining more attention as alternate approaches for high-throughput screening. Several cheminformatics tools are used in research, but integrating it with statistical methods are said to reflect the development of new algorithms and applications. These molecular modeling or cheminformatics methods strongly depend on the quantitative structure–activity relationship (QSAR) analysis. This QSAR technique is extensively applied to predict the pharmacokinetics property through the reference biological activity and it is one sound technique in the medicinal chemistry. Through this chapter, the basic principle of computational methods that relies on QSAR models, their descriptors, statistical phenomenon towards the molecular structures are discussed. At the same time, we also highlight the important components of QSAR models and their types to describe the molecular structure of lead molecules and discuss future limitations and perspectives to guide future research in the field of QSAR.

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References

  • Acharya C, Coop A, Polli JE, Mackerell AD Jr (2011) Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. Curr Comput Aided Drug Des 7:10–22

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Afantitis A, Melagraki G, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (2006) A novel QSAR model for predicting induction of apoptosis by 4-aryl-4H-chromenes. Bioorg Med Chem 14:6686–6694

    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 

  • Alam S, Khan F (2014) QSAR and docking studies on xanthone derivatives for anticancer activity targeting DNA topoisomerase II α. Drug Des Dev Ther 8:183–195

    Google Scholar 

  • Allen BK, Mehta S, Ember SW, Schonbrunn E, Ayad N, Schürer SC (2015) Large-scale computational screening identifies first in class multitarget inhibitor of EGFR kinase and BRD4. Sci Rep 5:16924

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Augen J (2002) The evolving role of information technology in the drug discovery process. Drug Discov Today 7:315–323

    Article  CAS  PubMed  Google Scholar 

  • Bajorath J (2002) Integration of virtual and high-throughput screening. Nat Rev Drug Discov 1:882–894

    Article  CAS  PubMed  Google Scholar 

  • Bajorath JR (ed) (2011) Chemoinformatics and computational chemical biology. Humana Press, Totowa

    Google Scholar 

  • Balaban AT (1982) Highly discriminating distance-based topological index. Chem Phys Lett 89:399–404

    Article  CAS  Google Scholar 

  • Ban F, Dalal K, Li H, LeBlanc E, Rennie PS, Cherkasov A (2017) Best practices of computer-aided drug discovery: lessons learned from the development of a preclinical candidate for prostate cancer with a new mechanism of action. J Chem Inf Model 57:1018–1028

    Article  CAS  PubMed  Google Scholar 

  • Baraldi PG (1999) Comparative molecular field analysis (CoMFA) of a series of selective adenosine receptor A2A antagonists. Drug Dev Res 46:126–133

    Article  CAS  Google Scholar 

  • Bauer MR, Ibrahim TM, Vogel SM, Boeckler FM (2013) Evaluation and optimization of virtual screening workflows with DEKOIS 2.0 – a public library of challenging docking benchmark sets. J Chem Inf Model 53:1447–1462

    Article  CAS  PubMed  Google Scholar 

  • Bissantz C, Folkers G, Rognan D (2000) Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. J Med Chem 43:4759–4767

    Article  CAS  PubMed  Google Scholar 

  • Bissantz C, Bernard P, Hibert M, Rognan D (2003) Protein-based virtual screening of chemical databases. II. Are homology models of G-protein coupled receptors suitable targets? Proteins 50:5–25

    Article  CAS  PubMed  Google Scholar 

  • Blum LC, Reymond JL (2009) 970 million druglike small molecules for virtual screening in the chemical universe database GDB-13. J Am Chem Soc 131:8732–8733

    Article  CAS  PubMed  Google Scholar 

  • Bolis G, Di Pace L, Fabrocini FJ (1991) A machine learning approach to computer-aided molecular design. Comput Aided Mol Des 5:617–628

    Article  CAS  Google Scholar 

  • Brignole EA, Bottini SB, Gani R (1986) A strategy for the solvents for liquid extraction of solvents for separation processes. Fluid Phase Equilib 29:125

    Article  CAS  Google Scholar 

  • Brozell SR, Mukherjee S, Balius TE, Roe DR, Case DA, Rizzo RC (2012) Evaluation of DOCK 6 as a pose generation and database enrichment tool. J Comput Aided Mol Des 26:749–773

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Catalin B (2014) More effective DPP4 inhibitors as antidiabetics based on sitagliptin applied QSAR and clinical methods. Curr Comput Aided Drug Des 10:237–249(13)

    Google Scholar 

  • Chang C, Swaan PW (2006) Computational approaches to modeling drug transporters. Eur J Pharm Sci 27:411–424

    Article  CAS  PubMed  Google Scholar 

  • Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57:4977–5010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Churchwell CJ, Rintoul MD, Martin S, Visco DP, Kotu A, Larson RS, Sillerud LO, Brown DC, Faulon J (2004) The signature molecular descriptor. 3. Inverse-quantitative structure-activity relationship of ICAM-1 inhibitory peptides. J Mol Graph Model 22:263–273

    Article  CAS  PubMed  Google Scholar 

  • Clark DE, Pickett SD (2000) Computational methods for the prediction of ‘drug-likeness’. Drug Discov Today 5:49–58

    Article  CAS  PubMed  Google Scholar 

  • Clark DE, Firth MA, Murray CW (1996) Molmaker: de novo generation of 3D databases for use in drug design. J Chem Inf Comput Sci 36:137

    Article  CAS  PubMed  Google Scholar 

  • Cramer RD, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967

    Article  CAS  PubMed  Google Scholar 

  • Crivori P, Cruciani G, Carrupt PA, Testa B (2000) Predicting blood–brain barrier permeation from three-dimensional molecular structure. J Med Chem 43:2204–2216

    Article  CAS  PubMed  Google Scholar 

  • Cruciani G, Crivori P, Carrupt PA, Testa B (2000) Molecular interaction fields in drug discovery: recent advances and future perspectives. J Mol Struct THEOCHEM 503:17–30

    Article  CAS  Google Scholar 

  • Crum-Brown AFT (1868) On the connection between chemical constitution and physiological action. Pt 1. On the physiological action of the salts of the ammonium bases, derived from strychnia, Brucia. Thebia, Codeia, Morphia and Nicotia. R Soc Edin 2:151–203

    Article  Google Scholar 

  • Cummings MD, DesJarlais RL, Gibbs AC, Mohan V, Jaeger EP (2005) Comparison of automated docking programs as virtual screening tools. J Med Chem 48:962–976

    Article  CAS  PubMed  Google Scholar 

  • de Groot MJ, Ekins S (2002) Pharmacophore modeling of cytochromes P450. Adv Drug Deliv Rev 54:367–383

    Article  PubMed  Google Scholar 

  • Dean PM (2005) Computer-aided design of small molecules for chemical genomics. Humana Press Inc., Totowa

    Book  Google Scholar 

  • Derringer GC, Markham RL (1985) A computer-based methodology for matching polymer structures with required properties. J Appl Polym Sci 30:4609–4617

    Article  CAS  Google Scholar 

  • Deshpande M, Kuramochi M, Karypis J (2002) Frequent substructure-based approaches for classifying chemical compounds. In: Proc of the 8th international conference on knowledge discovery and data mining, Edmonton

    Google Scholar 

  • Dessalew N, Singh SK (2008) 3D-QSAR CoMFA and CoMSIA study on benzodipyrazoles as cyclin dependent kinase 2 inhibitors. Med Chem 4:313–321

    Article  CAS  PubMed  Google Scholar 

  • Dessalew N, Bharatam PV, Singh SK (2007) 3D-QSAR CoMFA study on aminothiazole derivatives as cyclin-dependent kinase 2 inhibitors. QSAR Comb Sci 26:85–91

    Article  CAS  Google Scholar 

  • Diller DJ, Li R (2003) Kinases, homology models, and high throughput docking. J Med Chem 46:4638–4647

    Article  CAS  PubMed  Google Scholar 

  • Douali L, Villemin D, Cherqaoui D (2003) Neural networks: accurate nonlinear QSAR model for HEPT derivatives. J Chem Inf Comput Sci 43:1200–1207

    Article  CAS  PubMed  Google Scholar 

  • Ekins S, Lage de Siqueira-Neto J, McCall L-I, Sarker M, Yadav M, Ponder EL (2015) Machine learning models and pathway genome data base for Trypanosoma cruzi drug discovery. PLoS Negl Trop Dis 9:e0003878

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Empereur-mot C, Guillemain H, Latouche A, Zagury JF, Viallon V, Montes M (2015) Predictiveness curves in virtual screening. J Chem Informatics 7:52

    Google Scholar 

  • Eriksson L, Jaworska J, Worth AP, Cronin MT, McDowell RM, Gramatica P (2003) Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ Health Perspect 111:1361–1375

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Estrada E, Uriarte E (2001) Quantitative structure-toxicity relationships using TOPS-MODE. 1. Nitrobenzene toxicity to tetrahymena pyriformis. Environ Res 12:309–324

    CAS  Google Scholar 

  • Faulon JL, Visco DP Jr, Pophale RS (2003) The signature molecular descriptor. 1. Using extended valence sequences in QSAR and QSPR studies. J Chem Inf Comput Sci 43:707–720

    Article  CAS  PubMed  Google Scholar 

  • Fink T, Bruggesser H, Reymond JL (2005) Virtual exploration of the small-molecule chemical universe below 160 Daltons. Angew Chem Int Ed 44:1504–1508

    Article  CAS  Google Scholar 

  • Flower DR (2002a) Predicting chemical toxicity and fate. CRC Press, Roca Baton

    Google Scholar 

  • Flower DR (2002b) Drug design: cutting edge approaches. Royal Society of Chemistry, Cambridge

    Google Scholar 

  • Fourches D, Muratov E, Tropsha A (2015) Curation of chemogenomics data. Nat Chem Biol 11:535–535

    Article  CAS  PubMed  Google Scholar 

  • Furnival GM, Wilson RW (1974) Regressions by leaps and bounds. Technometrics 16:499–511

    Article  Google Scholar 

  • Gao H, Williams C, Labute P, Bajorath J (1999) Binary Quantitative structure−activity relationship (QSAR) analysis of estrogen receptor ligands. J Chem Inf Comput Sci 39:164

    Article  CAS  PubMed  Google Scholar 

  • Gasteiger J (2003) Handbook of chemoinformatics: from data to knowledge. Wiley, New York

    Book  Google Scholar 

  • Goh GB, Hodas NO, Vishnu A (2017) Deep learning for computational chemistry. J Comput Chem 38:1291–1307

    Article  CAS  PubMed  Google Scholar 

  • Gohda K, Mori I, Ohta D, Kikuchi T (2000) A CoMFA analysis with conformational propensity: an attempt to analyze the SAR of a set of molecules with different conformational flexibility using a 3D-QSAR method. J Comput Aided Mol Des 14:265–275

    Article  CAS  PubMed  Google Scholar 

  • Golbraikh A, Shen M, Xiao Z, Xiao YD, Lee KH, Tropsha A (2003) Rational selection of training and test sets for the development of validated QSAR models. J Comput Aided Mol Des 17:241–253

    Article  CAS  PubMed  Google Scholar 

  • Gómez-Bombarelli R, Wei JN, Duvenaud D, Hernandez-Lobato JM, Sanchez-Lengeling B, Sheberla D, Aguilera-Iparraguirre J, Hirzel TD, Adams RP, Aspuru-Guzik A (2018) Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci 4:268–276

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Good AC, Oprea TI (2008) Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection? J Comput Aided Mol Des 22:169–178

    Article  CAS  PubMed  Google Scholar 

  • Guner OF (2002) History and evolution of the pharmacophore concept in computer-aided drug design. Curr Top Med Chem 2:1321–1332

    Article  CAS  PubMed  Google Scholar 

  • Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389

    Article  Google Scholar 

  • Hall LH, Kier LBJ (2000) Chem Inf Comput Sci 30:784–791

    Article  CAS  Google Scholar 

  • Hall DG, Manku S, Wang F (2001) Solution- and solid-phase strategies for the design, synthesis, and screening of libraries based on natural product templates: a comprehensive survey. J Comb Chem 3:125–150

    Article  CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

  • Hasegawa K, Arakawab M, Funatsu K (2000) Rational choice of bioactive conformations through use of conformation analysis and 3-way partial least squares modeling. Chemom Intell Lab Syst 50:253–261

    Article  CAS  Google Scholar 

  • Hecht P (2002) High-throughput screening: beating the odds with informatics-driven chemistry. Curr Drug Discov 10:21–24

    Google Scholar 

  • Helguera AM, Combes RD, Gonzalez MP, Cordeiro MN (2008) Applications of 2D descriptors in drug design: a DRAGON tale. Curr Top Med Chem 8:1628–1655

    Article  CAS  PubMed  Google Scholar 

  • Hessler G, Zimmermann M, Matter H, Evers A, Naumann T, Lengauer T, Rarey M (2005) Multiple-ligand-based virtual screening: methods and applications of the MTree approach. J Med Chem 48:6575–6584

    Article  CAS  PubMed  Google Scholar 

  • Huang N, Shoichet BK, Irwin JJ (2006) Benchmarking sets for molecular docking. J Med Chem 49:6789–6801

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ibrahim TM, Bauer MR, Boeckler FM (2015) Applying DEKOIS 2.0 in structure-based virtual screening to probe the impact of preparation procedures and score normalization. Aust J Chem 7:21

    Google Scholar 

  • Irwin JJ (2008) Community benchmarks for virtual screening. J Comput Aided Mol Des 22:193–199

    Article  CAS  PubMed  Google Scholar 

  • Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748

    Article  CAS  PubMed  Google Scholar 

  • Kapetanovic IM (2008) Computer-aided drug discovery and development (CADDD): in-silico-chemico-biological approach. Chem Biol Interact 171:165–176

    Article  CAS  PubMed  Google Scholar 

  • Kellenberger E, Rodrigo J, Muller P, Rognan D (2004) Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins 57:225–242

    Article  CAS  PubMed  Google Scholar 

  • Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935

    Article  CAS  PubMed  Google Scholar 

  • Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem 37:4130–4146

    Article  CAS  PubMed  Google Scholar 

  • Koehn FE, Carter GT (2005) The evolving role of natural products in drug discovery. Nat Rev Drug Discov 2005:206–220

    Article  CAS  Google Scholar 

  • Kovatcheva A, Buchbauer G, Golbraikh A, Wolschann P (2003) QSAR modeling of alpha-campholenic derivatives with sandalwood odor. J Chem Inf Comput Sci 43:259–266

    Article  CAS  PubMed  Google Scholar 

  • Kumar Singh S, Dessalew N, Bharatam PV (2007) 3D-QSAR CoMFA study on oxindole derivatives as cyclin dependent kinase 1 (CDK1) and cyclin dependent kinase 2 (CDK2) inhibitors. Med Chem 3:75–84

    Article  Google Scholar 

  • Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161:269–288

    Article  CAS  PubMed  Google Scholar 

  • Labute PA (2000) Widely applicable set of descriptors. J Mol Graph Model 18:464–477

    Article  CAS  PubMed  Google Scholar 

  • Lee KW, Briggs JM (2001) Comparative molecular field analysis (CoMFA) study of epothilones-tubulin depolymerization inhibitors: phramacophore developemt using 3D QSAR methods. J Comput Aided Mol Des 15:41–55

    Article  CAS  PubMed  Google Scholar 

  • Lemmen C, Lengauer TJ (2000) Computational methods for the structural alignment of molecules. Comput Aided Mol Des 14:215–232

    Article  CAS  Google Scholar 

  • Lewis RA (2005) A general method for exploiting QSAR models in lead optimization. J Med Chem 48:1638–1648

    Article  CAS  PubMed  Google Scholar 

  • Li P, Tian Y, Zhai H, Deng F, Xie M, Zhang X (2013) Study on the activity of non-purine xanthine oxidase inhibitor by 3D-QSAR modeling and molecular docking. J Mol Struct 5:56–65

    Google Scholar 

  • Liu SS, Liu HL, Yin CS, Wang LSJ (2003) VSMP: a novel variable selection and modeling method based on the prediction. Chem Inf Comput Sci 43:964–969

    Article  CAS  Google Scholar 

  • MacKerell AD Jr (2004) Empirical force fields for biological macromolecules: overview and issues. J Comput Chem 25:1584–1604

    Article  CAS  PubMed  Google Scholar 

  • Makino S, Ewing TJA, Kuntz ID (1999) DREAM++: flexible docking program for virtual combinatorial libraries. J Comput Aided Mol Des 13:513–532

    Article  CAS  PubMed  Google Scholar 

  • Mandel J (1982) Use of the singular value decomposition in regression-analysis. Am Stat 36:15–24

    Google Scholar 

  • Matter H, Baringhaus KH, Naumann T, Klabunde T, Pirard B (2001) Computational approaches towards the rational design of drug-like compound libraries. Comb Chem High Scr 4:453–475

    CAS  Google Scholar 

  • McGovern SL, Shoichet BK (2003) Information decay in molecular docking screens against holo, apo, and modeled conformations of enzymes. J Med Chem 46:2895–2907

    Article  CAS  PubMed  Google Scholar 

  • Mitchell JBO (2004) Machine learning methods in chemoinformatics. Wiley Interdiscip Rev Comput Mol Sci 4:468–481

    Article  CAS  Google Scholar 

  • Moustakas DT, Lang PT, Pegg S, Pettersen E, Kuntz ID, Brooijmans N, Rizzo RC (2006) Development and validation of a modular, extensible docking program: DOCK 5. J Comput Aided Mol Des 20:601–619

    Article  CAS  PubMed  Google Scholar 

  • Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55(2012):6582–6594

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nantasenamat C, Monnor T, Worachartcheewan A, Mandi P, Isarankura-Na-Ayudhya C, Prachayasittikul V (2014) Predictive QSAR modeling of aldose reductase inhibitors using Monte Carlo feature selection. Eur J Med Chem 76:352–359

    Article  CAS  PubMed  Google Scholar 

  • Netzeva TI, Gallegos SA, Worth AP (2006) Comparison of the applicability domain of a quantitative structure-activity relationship for estrogenicity with a large chemical inventory. Environ Toxicol Chem 25:1223–1230

    Article  CAS  PubMed  Google Scholar 

  • Neves MA, Totrov M, Abagyan R (2012) Docking and scoring with ICM: the benchmarking results and strategies for improvement. J Comput Aided Mol Des 26:675–686

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nieto-Draghi C, Fayet G, Creton B, Rozanska X, Rotureau P, de Hemptinne JC, Ungerer P, Rousseau B, Adamo C (2015) A general guidebook for the theoretical prediction of physicochemical properties of chemicals for regulatory purposes. Chem Rev 115:13093–13164

    Article  CAS  PubMed  Google Scholar 

  • Oprea TI, Davis AM, Teague SJ, Leeson PD (2001) Is there a difference between leads and drugs? A historical perspective. J Chem Inf Comput Sci 41:1308–1315

    Article  CAS  PubMed  Google Scholar 

  • Panwar U, Singh SK (2020) Atom-based 3D-QSAR, molecular docking, DFT, and simulation studies of acylhydrazone, hydrazine, and diazene derivatives as IN-LEDGF/p75 inhibitors. Struct Chem 2020:1–16

    Google Scholar 

  • Pastor M, Cruciani G, McLay I, Pickett S, Clementi S (2000) GRid-INdependent Descriptors (GRIND): a novel class of alignment-independent three-dimensional molecular descriptors. J Med Chem 43:3233–3243

    Article  CAS  PubMed  Google Scholar 

  • Prado-Prado FJ, García I, García-Mera X, González-Díaz H (2011) Entropy multi-target QSAR model for prediction of antiviral drug complex networks. Chemom Intell Lab Syst 107:227–233

    Article  CAS  Google Scholar 

  • Prasanna S, Doerksen RJ (2009) Topological polar surface area: a useful descriptor in 2D-QSAR. Curr Med Chem 16:21–41

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pretel EJ, López PA, Bottini SB, Brignole EA (1994) Computer-aided molecular design of solvents for separation processes. AICHE J 40:1349–1360

    Article  CAS  Google Scholar 

  • Proudfoot JR (2002) Drugs, leads, and drug-likeness: an analysis of some recently launched drugs. Bioorg Med Chem Lett 12:1647–1650

    Article  CAS  PubMed  Google Scholar 

  • Prusis P, Dambrova M, Andrianov V, Rozhkov E, Semenikhina V, Piskunova I, Ongwae E, Lundstedt T, Kalvinsh I, Wikberg JES (2004) Synthesis and quantitative structure−activity relationship of hydrazones of N-amino-N‘-hydroxyguanidine as electron acceptors for xanthine oxidase. J Med Chem 47:3105–3110

    Article  CAS  PubMed  Google Scholar 

  • Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261:470–489

    Article  CAS  PubMed  Google Scholar 

  • Ravichandran V, Shalini S, Sundram KM, Dhanaraj SA (2010) QSAR study of substituted 1, 3, 4-oxadiazole naphthyridines as HIV-1 integrase inhibitors. Eur J Med Chem 45:2791–2797

    Article  CAS  PubMed  Google Scholar 

  • Reddy AS, Pati SP, Kumar PP, Pradeep HN, Sastry GN (2007) Virtual screening in drug discovery - a computational perspective. Curr Protein Pept Sci 8:329–351

    Article  CAS  PubMed  Google Scholar 

  • Reddy KK, Singh SK, Dessalew N, Tripathi SK, Selvaraj C (2012) Pharmacophore modelling and atom-based 3D-QSAR studies on N-methyl pyrimidones as HIV-1 integrase inhibitors. J Enzyme Inhib Med Chem 27:339–347

    Article  CAS  PubMed  Google Scholar 

  • Reddy KK, Singh SK, Tripathi SK, Selvaraj C (2013a) Identification of potential HIV-1 integrase strand transfer inhibitors: in silico virtual screening and QM/MM docking studies. SAR QSAR Environ Res 24:581–595

    Article  CAS  PubMed  Google Scholar 

  • Reddy KK, Singh SK, Tripathi SK, Selvaraj C, Suryanarayanan V (2013b) Shape and pharmacophore-based virtual screening to identify potential cytochrome P450 sterol 14α-demethylase inhibitors. J Recept Signal Transduction 33:234–243

    Article  CAS  Google Scholar 

  • Repasky MP, Murphy RB, Banks JL, Greenwood JR, Tubert-Brohman I, Bhat S, Friesner RA (2012) Docking performance of the glide program as evaluated on the Astex and DUD datasets: a complete set of glide SP results and selected results for a new scoring function integrating WaterMap and glide. J Comput Aided Mol Des 26:787–799

    Article  CAS  PubMed  Google Scholar 

  • Rester U (2006) Dock around the clock - current status of small molecule docking and scoring. QSAR Comb Sci 25:605–615

    Article  CAS  Google Scholar 

  • Roy K, Mitra I, Kar S, Ojha PK, Das RN, Kabir H (2012) Comparative studies on some metrics for external validation of QSAR model. J Chem Inf Model 52:396–408

    Article  CAS  PubMed  Google Scholar 

  • Roy K, Kar S, Das RN (2015) Background of QSAR and historical developments. In: Das KRKN (ed) Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. Academic, Boston, pp 1–46

    Google Scholar 

  • Ruddigkeit L, van Deursen R, Blum LC, Reymond JL (2012) Visualization and virtual screening of the chemical universe database GDB-17. J Chem Inf Model 52:2864

    Article  CAS  PubMed  Google Scholar 

  • Ruggeri C, Drinkwater N, Sivaraman KK, Bamert RS, McGowan S, Paiardini A (2011) Identification and validation of a potent dual inhibitor of the P. falciparum M1 and M17 aminopeptidases using virtual screening. PLoS ONE 10:e0138957

    Article  CAS  Google Scholar 

  • Rusinko A III, Young SS, Drewry DH, Gerritz SW (2002) Optimization of focused chemical libraries using recursive partitioning. Comb Chem High Scr 5:125–133

    CAS  Google Scholar 

  • Saliner AG, Netzeva TI, Worth AP (2006) Prediction of estrogenicity: validation of a classification model. Environ Res 17:195–223

    CAS  Google Scholar 

  • Santos-Filho OA, Hopfinger AJ (2001) A search for sources of drug resistance by the 4D-QSAR analysis of a set of antimalarial dihydrofolate reductase inhibitors. J Comput Aided Mol Des 15:1–12

    Article  CAS  PubMed  Google Scholar 

  • Schneider G, Baringhaus KH (2013) De novo design: from models to molecules. In: De novo molecular design. Wiley, Weinheim, pp 1–55

    Chapter  Google Scholar 

  • Schneider G, Fechner U (2005) Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov 4:649

    Article  CAS  PubMed  Google Scholar 

  • Selvaraj C, Singh P, Singh SK (2014) Molecular insights on analogs of HIV PR inhibitors toward HTLV‐1 PR through QM/MM interactions and molecular dynamics studies: comparative structure analysis of wild and mutant HTLV‐1 PR. J Mol Recognit 27:696–706

    Article  CAS  PubMed  Google Scholar 

  • Sharma S, Ravichandran V, Jain PK, Mourya VK, Agrawal RK (2008) Prediction of caspase-3 inhibitory activity of 1,3-dioxo-4-methyl-2,3- dihydro-1h-pyrrolo[3,4-c] quinolines: QSAR study. J Enzyme Inhib Med Chem 23:424–431

    Article  CAS  PubMed  Google Scholar 

  • Shen M, LeTiran A, Xiao Y, Golbraikh A, Kohn H, Tropsha A (2002) Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearest neighbor and simulated annealing PLS methods. J Med Chem 45:2811–2823

    Article  CAS  PubMed  Google Scholar 

  • Shen M, Xiao Y, Golbraikh A, Gombar VK, Tropsha A (2003) Development and validation of k-nearest-neighbor QSPR models of metabolic stability of drug candidates. J Med Chem 46:3013–3020

    Article  CAS  PubMed  Google Scholar 

  • Singh SK, Dessalew N, Bharatam PV (2006) 3D-QSAR CoMFA study on indenopyrazole derivatives as cyclin dependent kinase 4 (CDK4) and cyclin dependent kinase 2 (CDK2) inhibitors. Eur J Med Chem 41:1310–1319

    Article  CAS  PubMed  Google Scholar 

  • Skvortsova MI, Fedyaev KS, Palyulin VA, Zefirov N (2001) Inverse structure-property relationship problem for the case of a correlation equation containing the Hosoya index. Dokl Chem 379:191–195

    Article  Google Scholar 

  • Southan C, Várkonyi P, Muresan S (2009) Quantitative assessment of the expanding complementarity between public and commercial databases of bioactive compounds. J Chem 1:10

    CAS  Google Scholar 

  • Speck-Planche A, Cordeiro MNDS (2017) Fragment-based in silico modeling of multi-target inhibitors against breast cancer-related proteins. Mol Divers 21:511–523

    Article  CAS  PubMed  Google Scholar 

  • Spitzer R, Jain AN (2012) Surflex-dock: docking benchmarks and real-world application. J Comput Aided Mol Des 26:687–699

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Stanton DT, Egolf LM, Jurs PC, Hicks MG (1992) Computer assisted prediction of normal boiling points of pyrans and pyrroles. J Chem Inf Comput Sci 32:306–316

    Article  CAS  Google Scholar 

  • Stumpfe D, Bajorath J (2011) Applied virtual screening: strategies, recommendations, and caveats. In: Sotriffer C (ed) Virtual screening: principles, challenges, and practical guidelines. Wiley, Weinheim, pp 291–318

    Chapter  Google Scholar 

  • Suryanarayanan V, Kumar Singh S, Kumar Tripathi S, Selvaraj C, Konda Reddy K, Karthiga A (2013) A three-dimensional chemical phase pharmacophore mapping, QSAR modelling and electronic feature analysis of benzofuran salicylic acid derivatives as LYP inhibitors. Environ Res 24:1025–1040

    CAS  Google Scholar 

  • Tang H, Yang L, Li J, Chen J (2016) Molecular modelling studies of 3,5- dipyridyl-1,2,4-triazole derivatives as xanthine oxidoreductase inhibitors using 3D-QSAR, TopomerCoMFA, molecular docking and molecular dynamic simulations. J Taiwan Inst Chem Eng 68:64–73

    Article  CAS  Google Scholar 

  • Tong W, Lowis DR, Perkins R, Chen Y, Welsh WJ, Goddette DW, Heritage TW, Sleehan DM (1998) Evaluation of quantitative structure-activity relationship methods for large-scale prediction of chemicals binding to the estrogen receptor. J Chem Inf Comput Sci 38:669

    Article  CAS  PubMed  Google Scholar 

  • Tong W, Xie Q, Hong H, Shi L, Fang H, Perkins R (2004) Assessment of prediction confidence and domain extrapolation of two structure-activity relationship models for predicting estrogen receptor binding activity. Environ Health Perspect 112:1249–1254

    CAS  PubMed  PubMed Central  Google Scholar 

  • Triballeau N, Acher F, Brabet I, Pin J-P, Bertrand H-O (2005) Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J Med Chem 48:2534–2547

    Article  CAS  PubMed  Google Scholar 

  • Tripos (2007) SYBYL8.0. In: Discovery software for computational chemistry and molecular modeling. St. Louis, Missouri, USA

    Google Scholar 

  • Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inform 29:476–488

    Article  CAS  PubMed  Google Scholar 

  • Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. Quant Struct Act Relat Comb Sci 22:69–77

    CAS  Google Scholar 

  • Van Drie JH (2003) Pharmacophore discovery: lessons learned. Curr Pharm Des 9:1649–1664

    Article  PubMed  Google Scholar 

  • Varnek A, Baskin II (2011a) Chemoinformatics as a theoretical chemistry discipline. Mol Inf 30:20–32

    Article  CAS  Google Scholar 

  • Varnek A, Baskin II (2011b) Chemoinformatics as a theoretical chemistry discipline. Mol Informatics 30:20–32

    Article  CAS  Google Scholar 

  • Veerasamy R, Rajak H, Jain A, Sivadasan S, Varghese CP, Agrawal RK (2011) Validation of QSAR models - strategies and importance. Int J Drug Des Discov 2:511–519

    CAS  Google Scholar 

  • Verdonk ML, Berdini V, Hartshorn MJ, Mooij WT, Murray CW, Taylor RD, Watson P (2004) Virtual screening using protein-ligand docking: avoiding artificial enrichment. J Chem Inf Comput Sci 44:793–806

    Article  CAS  PubMed  Google Scholar 

  • Verma J, Khedkar VM, Coutinho EC (2010) 3D-QSAR in drug design- a review. Curr Top Med Chem 10:95–115

    Article  CAS  PubMed  Google Scholar 

  • Vijaya Prabhu S, Singh SK (2018) Atom-based 3D-QSAR, induced fit docking, and molecular dynamics simulations study of thieno [2, 3-b] pyridines negative allosteric modulators of mGluR5. J Recept Signal Transduction 38:225–239

    Article  CAS  Google Scholar 

  • Vogel SM, Bauer MR, Boeckler FM (2011) DEKOIS: demanding evaluation kits for objective in silico screening—a versatile tool for benchmarking docking programs and scoring functions. J Chem Inf Model 51:2650–2665

    Article  CAS  PubMed  Google Scholar 

  • Wedebye EB, Dybdahl M, Nikolov NG, Jonsdottir SO, Niemela JR (2015) QSAR screening of 70, 983 REACH substance for genotoxic carcinogenicity, mutagenicity and development toxicity in the Chem Screen project. Reprod Toxicol 55:64–72

    Article  CAS  PubMed  Google Scholar 

  • Wegner JK, Frö hlich H, Zell AJ (2004) Feature selection for descriptor based classification models. 2. Human intestinal absorption (HIA). Chem Inf Comput Sci 44:921

    Article  CAS  Google Scholar 

  • Weis DC, Faulon JL, LeBorne RC, Visco DP (2005) The signature molecular descriptor. 5. The design of hydrofluoroether foam blowing agents using inverse-QSAR. Ind Eng Chem Res 44:8883–8891

    Article  CAS  Google Scholar 

  • Williams AJ, Ekins S (2011) A quality alert and call for improved curation of public chemistry databases. Drug Discov Today 16(2011):747–750

    Article  CAS  PubMed  Google Scholar 

  • Xu J, Hagler A (2002) Chemoinformatics and drug discovery. Molecules 7:566–600

    Article  CAS  PubMed Central  Google Scholar 

  • Xu J, Stevenson J (2000) Drug-like index: a new approach to measure drug-like compounds and their diversity. J Chem Inf Comput Sci 40:1177–1187

    Article  CAS  PubMed  Google Scholar 

  • Xu L, Zhang WJ (2001) Comparison of different methods for variable selection. Anal Chim Acta 446:475–481

    Article  Google Scholar 

  • Xue L, Bajorath J (2000) Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Comb Chem 3:363–372

    CAS  Google Scholar 

  • Yang SP, Song ST, Tang ZM, Song HF (2003) Optimization of antisense drug against conservative local motif in simulant secondary structures of HER-2 mRNA and QSAR analysis. Acta Pharmacol Sin 24:897–902

    CAS  PubMed  Google Scholar 

  • Yasuo K, Yamaotsu N, Gouda H, Tsujishita H, Hirono S (2009) Structure-based CoMFA as a predictive model - CYP2C9 inhibitors as a test case. J Chem Inf Model 49:853–864

    Article  CAS  PubMed  Google Scholar 

  • Young D, Martin T, Venkatapathy R, Harten P (2008) Are the chemical structures in your QSAR correct? QSAR Comb Sci 27:1337–1345

    Article  CAS  Google Scholar 

  • Zhang S, Golbraikh A, Tropsha A (2006) Development of quantitative structure-binding affinity relationship models based on novel geometrical chemical descriptors of the protein-ligand interfaces. J Med Chem 49:2713–2724

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang S, Wei L, Bastow K, Zheng W, Brossi A, Lee KH, Tropsha A (2007) Antitumor Agents 252. Application of validated QSAR models to database mining: discovery of novel tylophorine derivatives as potential anticancer agents. J Comput Aided Mol Des 21:97–112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Vivek Srivastava thankfully acknowledge to Department of Biotechnology, Faculty of Engineering and Technology, Rama University Uttar Pradesh, Kanpur, India and also Chandrabose Selvaraj and Sanjeev Kumar Singh thankfully acknowledge to RUSA-Phase 2.0 Policy (TNmulti-Gen), Dept. of Edn, Govt. of India (Grant No: F.24-51/2014-U).

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Authors and Affiliations

  1. Department of Biotechnology, Faculty of Engineering and Technology, Rama University Uttar Pradesh, Kanpur, Uttar Pradesh, India

    Vivek Srivastava

  2. Computer Aided Drug Design and Molecular Modeling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamilnadu, India

    Chandrabose Selvaraj & Sanjeev Kumar Singh

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  1. Vivek Srivastava
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  2. Chandrabose Selvaraj
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  3. Sanjeev Kumar Singh
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Correspondence to Chandrabose Selvaraj .

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Editors and Affiliations

  1. Department of Biosciences, Indrashil University, Mehsana, Gujarat, India

    Vijai Singh

  2. Biotechnology, Rama University, Kanpur, Uttar Pradesh, India

    Ajay Kumar

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Srivastava, V., Selvaraj, C., Singh, S.K. (2021). Chemoinformatics and QSAR. In: Singh, V., Kumar, A. (eds) Advances in Bioinformatics. Springer, Singapore. https://doi.org/10.1007/978-981-33-6191-1_10

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