Quantitative structure-activity relationship: Promising advances in drug discovery platforms

Expert Opinion on Drug Discovery

Volumn 10, Issue 12, 2015, Pages 1283-1300

  Wang, Tao ^a   Wu, Mian Bin ^a   Lin, Jian Ping ^a   Yang, Li Rong ^a  

^a ZHEJIANG UNIVERSITY   (China)

Author keywords

Comparative molecular field analysis; comparative molecular similarity indices analysis; machine learning; quantitative structure activity relationship; rational design; virtual screening

Indexed keywords

ANTIMALARIAL AGENT; BETA LACTAMASE INHIBITOR; HUMAN IMMUNODEFICIENCY VIRUS PROTEINASE INHIBITOR; PENETRATION ENHANCING AGENT; RNA DIRECTED DNA POLYMERASE INHIBITOR; THROMBOXANE A2 RECEPTOR BLOCKING AGENT;

ANTIMALARIAL ACTIVITY; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL ACTIVITY; COMPARATIVE MOLECULAR FIELD ANALYSIS; COMPARATIVE MOLECULAR SIMILARITY INDICES ANALYSIS; DRUG DESIGN; DRUG DEVELOPMENT; HYDROGEN BOND; LIGAND BINDING; MOLECULAR DYNAMICS; PHARMACOPHORE; PRIORITY JOURNAL; PROTEIN STRUCTURE; PROTON TRANSPORT; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REVIEW; STEREOISOMERISM; SUPPORT VECTOR MACHINE; TAUTOMER; CHEMICAL STRUCTURE; COMPUTER AIDED DESIGN; COMPUTER PROGRAM; COMPUTER SIMULATION; HUMAN; MOLECULARLY TARGETED THERAPY; PROCEDURES;

COMPUTER SIMULATION; COMPUTER-AIDED DESIGN; DRUG DESIGN; DRUG DISCOVERY; HUMANS; MODELS, MOLECULAR; MOLECULAR TARGETED THERAPY; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SOFTWARE;

EID: 84948568727     PISSN: 17460441     EISSN: 1746045X     Source Type: Journal    
DOI: 10.1517/17460441.2015.1083006     Document Type: Review

References (157)

1. Organization WH. Antimicrobial resistance: global report on surveillance. World Health Organization; 2014
2. Johnson VA, Calvez V, Günthard HF, et al. 2011 update of the drug resistance mutations in HIV-1. Top Antivir Med 2011; 19: 156-64
3. Pfaller MA. Antifungal drug resistance: mechanisms, epidemiology, and consequences for treatment. Am J Med 2012; 125: S3-S13
4. Baguley BC. Multiple drug resistance mechanisms in cancer. Mol Biotechnol 2010; 46: 308-16
5. DiMasi JA, Feldman L, Seckler A, et al. Trends in risks associated with new drug development: success rates for investigational drugs. Clin Pharmacol Ther 2010; 87: 272-7
6. Begley CG, Ellis LM. Drug development: Raise standards for preclinical cancer research. Nature 2012; 483: 531-3
7. Hait WN. Anticancer drug development: the grand challenges. Nat Rev Drug Discov 2010; 9: 253-4
8. Vijayakrishnan R. Structure-based drug design and modern medicine. J Postgrad Med 2009; 55: 301-4
9. Talele TT, Khedkar SA, Rigby AC. Successful applications of computer aided drug discovery: moving drugs from concept to the clinic. Curr Top Med Chem 2010; 10: 127-41
10. Van Drie JH. Computer-aided drug design: the next 20 years. J Comput Aided Mol Des 2007; 21: 591-601
11. Jorgensen WL. The many roles of computation in drug discovery. Science 2004; 303: 1813-18
12. Kitchen DB, Decornez H, Furr JR, et al. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 2004; 3: 935-49
13. Clark DE, Pickett SD. Computational methods for the prediction of drug-likeness. Drug Discov Today 2000; 5: 49-58
14. Eagling V, Back D, Barry M. Differential inhibition of cytochrome P450 isoforms by the protease inhibitors, ritonavir, saquinavir and indinavir. Br J Clin Pharmacol 1997; 44: 190-4
15. Hartman GD, Egbertson MS, Halczenko W, et al. Non-peptide fibrinogen receptor antagonists. 1. Discovery and design of exosite inhibitors. J Med Chem 1992; 35: 4640-2
16. Gehlhaar DK, Moerder KE, Zichi D, et al. De novo design of enzyme inhibitors by Monte Carlo ligand generation. J Med Chem 1995; 38: 466-72
17. Sawyer JS, Anderson BD, Beight DW, et al. Synthesis and activity of new aryl-and heteroaryl-substituted pyrazole inhibitors of the transforming growth factor-β type I receptor kinase domain. J Med Chem 2003; 46: 3953-6
18. Shekhar C. In silico pharmacology: computer-aided methods could transform drug development. Chem Biol 2008; 15: 413-14
19. Balakrishnan A, Polli JE. Apical sodium dependent bile acid transporter (ASBT, SLC10A2): a potential prodrug target. Mol Pharm 2006; 3: 223-30
20. Zhong S, Macias AT, MacKerell AD. Computational identification of inhibitors of protein-protein interactions. Curr Top Med Chem 2007; 7: 63-82
21. Chen F, Hancock CN, Macias AT, et al. Characterization of ATP-independent ERK inhibitors identified through in silico analysis of the active ERK2 structure. Bioorg Med Chem Lett 2006; 16: 6281-7
22. Matthews TP, Jones AM, Collins I. Structure-based design, discovery and development of checkpoint kinase inhibitors as potential anticancer therapies. Expert Opin Drug Discov 2013; 8: 621-40
23. Cain R, Narramore S, McPhillie M, et al. Applications of structure-based design to antibacterial drug discovery. Bioorg Chem 2014; 55: 69-76
24. Du J, Cross TA, Zhou HX. Recent progress in structure-based anti-influenza drug design. Drug Discov Today 2012; 17: 1111-20
25. Claffey MM, Helal CJ, Verhoest PR, et al. Application of structure-based drug design and parallel chemistry to identify selective, brain penetrant, in vivo active phosphodiesterase 9A inhibitors. J Med Chem 2012; 55: 9055-68
26. Roche O, Sarmiento RM. A new class of histamine H3 receptor antagonists derived from ligand based design. Bioorg Med Chem Lett 2007; 17: 3670-5
27. Nandi S, Bagchi MC. 3D-QSAR and molecular docking studies of 4-anilinoquinazoline derivatives: a rational approach to anticancer drug design. Mol Divers 2010; 14: 27-38
28. Swann SL, Brown SP, Muchmore SW, et al. A unified, probabilistic framework for structure-and ligand-based virtual screening. J Med Chem 2011; 54: 1223-32
29. Noh SM, Atanasov AG, Schuster D, et al. Discovery of a novel IKK-b inhibitor by ligand-based virtual screening techniques. Bioorg Med Chem Lett 2011; 21: 577-83
30. Neves BJ, Bueno RV, Braga RC, et al. Discovery of new potential hits of Plasmodium falciparum enoyl-ACP reductase through ligand-and structure-based drug design approaches. Bioorg Med Chem Lett 2013; 23: 2436-41
31. Johnson MA, Maggiora GM. Concepts and applications of molecular similarity. Wiley; New York: 1990
32. Sliwoski G, Kothiwale S, Meiler J, et al. Computational Methods in Drug Discovery. Pharmacol Rev 2014; 66: 334-95
33. Sheridan RP, Kearsley SK. Why do we need so many chemical similarity search methods Drug Discov Today 2002; 7: 903-11
34. Bender A, Glen RC. Molecular similarity: a key technique in molecular informatics. Org Biomol Chem 2004; 2: 3204-18
35. Maldonado AG, Doucet J, Petitjean M, et al. Molecular similarity and diversity in chemoinformatics: from theory to applications. Mol Divers 2006; 10: 39-79
36. Geppert H, Vogt M, Bajorath J. Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J Chem Inf Model 2010; 50: 205-16
37. Kim KH, Kim ND, Seong BL. Pharmacophore-based virtual screening: a review of recent applications. Expert Opin Drug Discov 2010; 5: 205-22
38. Lee CH, Huang HC, Juan HF. Reviewing ligand-based rational drug design: the search for an ATP synthase inhibitor. Int J Mol Sci 2011; 12: 5304-18
39. Ripphausen P, Nisius B, Bajorath J. State-of-the-art in ligand-based virtual screening. Drug Discov Today 2011; 16: 372-6
40. Hansch C, Fujita T. p-σ-π Analysis. A method for the correlation of biological activity and chemical structure. J Am Chem Soc 1964; 86: 1616-26
41. Handen JS. The industrialization of drug discovery. Drug Discov Today 2002; 7: 83-5
42. Gaulton A, Bellis LJ, Bento AP, et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 2012. 40: 1100-7
43. ChEMBL. Available fromml: www.ebi.ac.uk/chembl
44. Butkiewicz M Jr, Mueller R, et al. Benchmarking ligand-based virtual high-throughput screening with the PubChem database. Molecules 2013. 18: 735-56
45. PubChem. http://pubchem.ncbi.nlm.nih.gov/
46. Schneider G, Fechner U. Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov 2005; 4: 649-63
47. Todeschini R, Consonni V. Handbook of molecular descriptors. John Wiley & Sons; New York: 2008. p. 11
48. Pearlman RS, Smith KM. Metric validation and the receptor-relevant subspace concept. J Chem Inf Compu Sci 1999; 39: 28-35
49. Hong H, Xie Q, Ge W, et al. Mold2, molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics. J Chem Inf Model 2008; 48: 1337-44
50. Todeschini R, Consonni V. Molecular Descriptors for Chemoinformatics. 2 Volume Set John Wiley & Sons; Chichester: 2009. 41
51. Bauerschmidt S, Gasteiger J. Overcoming the limitations of a connection table description: A universal representation of chemical species. J Chem Inf Compu Sci 1997; 37: 705-14
52. Glen RC. A fast empirical method for the calculation of molecular polarizability. J Comput Aided Mol Des 1994; 8: 457-66
53. Wang R, Gao Y, Lai L. Calculating partition coefficient by atom-additive method. Perspect Drug Discov Des 2000; 19: 47-66
54. Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 2007; 152: 9-20
55. Martínez-Santiago O, Millán-Cabrera R, Marrero-Ponce Y, et al. Discrete derivatives for atom-pairs as a novel graph-theoretical invariant for generating new molecular descriptors: orthogonality, interpretation and QSARs/QSPRs on benchmark databases. Mol Inform 2014; 33: 343-68
56. Ortega-Broche SE, Marrero-Ponce Y, Diáz YE, et al. Tomocomd-camps and protein bilinear indices-novel bio-macromolecular descriptors for protein research: I. Predicting protein stability effects of a complete set of alanine substitutions in the Arc repressor. Febs Journal 2010; 277: 3118-46
57. Randić M. Generalized molecular descriptors. J Math Chem 1991; 7: 155-68
58. Bajorath J. Chemoinformatics: concepts, methods, and tools for drug discovery. Springer Science & Business Media: Totowa, NJ: 2004. 275
59. Acharya C, Coop A, Polli JE, et al. Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. Curr Comput Aided Drug Des 2011; 7: 10-22
60. Basheer I, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 2000; 43: 3-31
61. Wold S, Esbensen K, Geladi P. Principal component analysis. Chemometrics Intellig Lab Syst 1987; 2: 37-52
62. Kubinyi H. QSAR and 3D QSAR in drug design Part 1: methodology. Drug Discov Today 1997; 2: 457-67
63. Geladi P, Kowalski BR. Partial least-squares regression: a tutorial. Anal Chim Acta 1986; 185: 1-17
64. Chang C, Swaan PW. Computational approaches to modeling drug transporters. Eur J Pharm Sci 2006; 27: 411-24
65. Kubinyi H. 3D QSAR in drug design: volume 1: theory methods and applications. Springer Science & Business Media; Totowa, NJ: 1993. 1
66. Cramer RD, Patterson DE, Bunce JD. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 1988; 110: 5959-67
67. Klebe G, Abraham U, Mietzner T. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem 1994; 37: 4130-46
68. Hechinger M, Leonhard K, Marquardt W. What is Wrong with Quantitative Structure-Property Relations Models Based on Three-Dimensional Descriptors J Chem Inf Model 2012; 52: 1984-93
69. Flower DR. Drug Design: Cutting Edge Approaches. Royal Society of Chemistry 2002; 279
70. Klebe G, Abraham U. Comparative molecular similarity index analysis (CoMSIA) to study hydrogen-bonding properties and to score combinatorial libraries. J Comput Aided Mol Des 1999; 13: 1-10
71. Bernard D, Coop A, MacKerell AD. 2D Conformationally sampled pharmacophore: A ligand-based pharmacophore to differentiate δ opioid agonists from antagonists. J Am Chem Soc 2003; 125: 3101-7
72. Bernard D, Coop A, MacKerell AD. Quantitative conformationally sampled pharmacophore for δ opioid ligands: reevaluation of hydrophobic moieties essential for biological activity. J Med Chem 2007; 50: 1799-809
73. Nicklaus MC, Wang S, Driscoll JS, et al. Conformational changes of small molecules binding to proteins. Biorg Med Chem 1995; 3: 411-28
74. Leach AR. Molecular modelling: principles and applications. Pearson Education; Harlow, England: 2001
75. Ghose A, Jaeger E, Kowalczyk P, et al. Conformational searching methods for small molecules. I. Study of the SYBYL SEARCH Method. J Comput Chem 1993; 14: 1050-65
76. Lipton M, Still WC. The multiple minimum problem in molecular modeling. Tree searching internal coordinate conformational space. J Comput Chem 1988; 9: 343-55
77. Chen IJ, Foloppe N. Conformational sampling of drug-like molecules with MOE and catalyst: implications for pharmacophore modeling and virtual screening. J Chem Inf Model 2008; 48: 1773-91
78. Ferguson DM, Raber DJ. A new approach to probing conformational space with molecular mechanics: random incremental pulse search. J Am Chem Soc 1989; 111: 4371-8
79. Blaney JM, Dixon JS. Distance geometry in molecular modeling. Rev Comput Chem 2007; 5: 299-335
80. Nair N, Goodman JM. Genetic algorithms in conformational analysis. J Chem Inf Comput Sci 1998; 38: 317-20
81. Corcho FJ, Filizola M, Pérez JJ. Evaluation of the iterative simulated annealing technique in conformational search of peptides. Chem Phys Lett 2000; 319: 65-70
82. Guarnieri F, Weinstein H. Conformational memories and the exploration of biologically relevant peptide conformations: an illustration for the gonadotropin-releasing hormone. J Am Chem Soc 1996; 118: 5580-9
83. Cvijovic D, Klinowski J. Taboo search: an approach to the multiple minima problem. Science 1995; 267: 664-6
84. Acharya C, Coop A, Polli JE, et al. Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. Curr Comput Aided Drug Des 2011; 7: 10-22
85. Vedani A, Briem H, Dobler M, et al. Multiple-conformation and protonation-state representation in 4D-QSAR: the neurokinin-1 receptor system. J Med Chem 2000; 43: 4416-27
86. Ekins S, Bravi G, Binkley S, et al. Three-and four-dimensional-quantitative structure activity relationship (3D/4D-QSAR) analyses of CYP2C9 inhibitors. Drug Metab Disposition 2000; 28: 994-1002
87. Lill MA. Multi-dimensional QSAR in drug discovery. Drug Discov Today 2007; 12: 1013-17
88. Hopfinger A, Wang S, Tokarski JS, et al. Construction of 3D-QSAR models using the 4D-QSAR analysis formalism. J Am Chem Soc 1997; 119: 10509-24
89. Hopfinger A. Inhibition of dihydrofolate reductase: structure-activity correlations of 2, 4-diamino-5-benzylpyrimidines based upon molecular shape analysis. J Med Chem 1981; 24: 818-22
90. Albuquerque MG, Hopfinger AJ, Barreiro E, et al. Four-dimensional quantitative structure-activity relationship analysis of a series of interphenylene 7-oxabicycloheptane oxazole thromboxane A2 receptor antagonists. J Chem Inf Computr Sci 1998; 38: 925-38
91. Andrade CH, Pasqualoto KF, Ferreira EI, et al. Rational design and 3D-pharmacophore mapping of 5-thiourea-substituted alpha-thymidine analogues as mycobacterial TMPK inhibitors. J Chem Inf Model 2009; 49: 1070-8
92. Liu J, Pan D, Tseng Y, et al. 4D-QSAR analysis of a series of antifungal p450 inhibitors and 3D-pharmacophore comparisons as a function of alignment. J Chem Inf Comput Sci 2003; 43: 2170-9
93. Krasowski MD, Hong X, Hopfinger A, et al. 4D-QSAR analysis of a set of propofol analogues: mapping binding sites for an anesthetic phenol on the GABAA receptor. J Med Chem 2002; 45: 3210-21
94. González-Diáz H, Prado-Prado FJ, Santana L, et al. Unify QSAR approach to antimicrobials. Part 1: predicting antifungal activity against different species. Bioorg Med Chem 2006; 14: 5973-80
95. Zanni R, Galvez-Llompart M, Galvez J, et al. QSAR multi-target in drug discovery: a review. Curr Comput Aided Drug Des 2014; 10: 129-36
96. Garcia I, Fall Y, Gomez G, Gonzalez-Diaz H. First computational chemistry multi-target model for anti-Alzheimer, anti-parasitic, anti-fungi, and anti-bacterial activity of GSK-3 inhibitors in vitro, in vivo, and in different cellular lines. Mol Divers 2011; 15: 561-7
97. Marzaro G, Chilin A, Guiotto A, et al. Using the TOPS-MODE approach to fit multi-target QSAR models for tyrosine kinases inhibitors. Eur J Med Chem 2011; 46: 2185-92
98. Prado-Prado FJ, Garciá I, Garciá-Mera X, et al. Entropy multi-target QSAR model for prediction of antiviral drug complex networks. Chemometr Intell Lab 2011; 107: 227-33
99. Prado-Prado FJ. Multi-target spectral moment: QSAR for antiviral drugs vs. different viral species. Anal Chim Acta 2009; 651: 159-64
100. Roy K, Roy PP. Comparative chemometric modeling of cytochrome 3A4 inhibitory activity of structurally diverse compounds using stepwise MLR, FA-MLR, PLS, GFA, G/PLS and ANN techniques. Eur J Med Chem 2009; 44: 2913-22
101. Tenorio-Borroto E, Peuelas Rivas CG, et al. ANN multiplexing model of drugs effect on macrophages; theoretical and flow cytometry study on the cytotoxicity of the anti-microbial drug G1 in spleen. Bioorg Med Chem 2012; 20: 6181-94
102. Sugaya N. Ligand efficiency-based support vector regression models for predicting bioactivities of ligands to drug target proteins. J Chem Inf Model 2014; 54: 2751-63
103. Heikamp K, Bajorath J. Support vector machines for drug discovery. Expert Opin Drug Discov 2014; 9: 93-104
104. Hu YJ, Ku TH, Jan RH, et al. Decision tree-based learning to predict patient controlled analgesia consumption and readjustment. BMC Med Inform Decis Mak 2012; 12: 131
105. Tetko IV, Sushko I, Pandey AK, et al. Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection. J Chem Inf Model 2008; 48: 1733-46
106. Benigni R, Bossa C. Predictivity of QSAR. J Chem Inf Model 2008; 48: 971-80
107. Cao DS, Liang YZ, Xu QS, et al. A new strategy of outlier detection for QSAR/QSPR. J Comput Chem 2010; 31: 592-602
108. Zhang Y, Meratnia N, Havinga P. Outlier detection techniques for wireless sensor networks: A survey. Communications Surveys & Tutorials. IEEE 2010; 12: 159-70
109. Subramaniam S, Palpanas T, Papadopoulos D, et al. Online outlier detection in sensor data using non-parametric models. In Proceedings of the 32nd international conference on Very large data bases. VLDB Endowment 2006. p. 187-98
110. Tropsha A, Gramatica P, Gombar VK. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR & Comb Sci 2003; 22: 69-77
111. Kohavi R. In a study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai 1995; 1137-45
112. Weiss S, Kulikowski C. Computer systems that learn. Morgan Kaufmann, San Mateo, California: 1991
113. Cronin MT. Predicting chemical toxicity and fate. CRC press. CRC Press, Boca Raton, Florida: 2004
114. Golbraikh A, Tropsha A, Predictive. QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. Mol Divers 2000; 5: 231-43
115. Gramatica P, Papa E. QSAR modeling of bioconcentration factor by theoretical molecular descriptors. QSAR & Comb Sci 2003; 22: 374-85
116. Gramatica P, Pilutti P, Papa E. QSAR prediction of ozone tropospheric degradation. QSAR & Comb Sci 2003; 22: 364-73
117. Roy K, Mitra I, Kar S, et al. Comparative studies on some metrics for external validation of QSPR models. J Chem Inf Model 2012; 52: 396-408
118. Mitra I, Roy PP, Kar S, et al. On further application of r m2 as a metric for validation of QSAR models. J Chemometrics 2010; 24: 22-33
119. Tropsha A, Golbraikh A. Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr Pharm Des 2007; 13: 3494-504
120. Narayana Moorthy NS, Ramos MJ, et al. Comparative structural analysis of alpha-glucosidase inhibitors on difference species: a computational study. Arch Pharm (Weinheim) 2012; 345: 265-74
121. Zhang L, Zhu H, Oprea TI, et al. QSAR modeling of the blood-brain barrier permeability for diverse organic compounds. Pharm Res 2008; 25: 1902-14
122. Chen B, Sheridan RP, Hornak V, et al. Comparison of random forest and Pipeline Pilot Naive Bayes in prospective QSAR predictions. J Chem Inf Model 2012; 52: 792-803
123. Sheridan RP. Time-split cross-validation as a method for estimating the goodness of prospective prediction. J Chem Inf Model 2013; 53: 783-90
124. Medina-Franco JL, Golbraikh A, Oloff S, et al. Quantitative structure-activity relationship analysis of pyridinone HIV-1 reverse transcriptase inhibitors using the k nearest neighbor method and QSAR-based database mining. J Comput Aided Mol Des 2005; 19: 229-42
125. Oloff S, Mailman RB, Tropsha A. Application of validated QSAR models of D1 dopaminergic antagonists for database mining. J Med Chem 2005; 48: 7322-32
126. Zhang S, Wei L, Bastow K, et al. 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 2007; 21: 97-112
127. Zhang L, Fourches D, Sedykh A, et al. Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening. J Chem Inf Model 2013; 53: 475-92
128. Zhang L, Sedykh A, Tripathi A, et al. Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR-and structure-based virtual screening approaches. Toxicol Appl Pharmacol 2013; 272: 67-76
129. Golla S, Neely BJ, Whitebay E, et al. Virtual design of chemical penetration enhancers for transdermal drug delivery. Chem Biol Drug Des 2012; 79: 478-87
130. Puzyn T, Leszczynski J, Cronin MT. Recent advances in QSAR studies: methods and applications. Springer Science & Business Media; New York: 2010. p. 8
131. Polishchuk PG, Muratov EN, Artemenko AG, et al. Application of random forest approach to QSAR prediction of aquatic toxicity. J Chem Inf Model 2009; 49: 2481-8
132. Cronin MT, Jaworska JS, Walker JD, et al. Use of QSARs in international decision-making frameworks to predict health effects of chemical substances. Environ Health Perspect 2003; 111: 1391
133. Xu S, Nirmalakhandan N. Use of QSAR models in predicting joint effects in multi-component mixtures of organic chemicals. Water Res 1998; 32: 2391-9
134. Junghans M, Backhaus T, Faust M, et al. Predictability of combined effects of eight chloroacetanilide herbicides on algal reproduction. Pest Manage Sci 2003; 59: 1101-10
135. Shao X, Li Z, Qian X, et al. Design, synthesis, and insecticidal activities of novel analogues of neonicotinoids: replacement of nitromethylene with nitroconjugated system. J Agric Food Chem 2009; 57: 951-7
136. Montesinos E, Bardaji E. Synthetic Antimicrobial Peptides as Agricultural Pesticides for Plant-Disease Control. Chem Biodivers 2008; 5: 1225-37
137. Kirchmair J, Williamson MJ, Tyzack JD, et al. Computational prediction of metabolismml: sites, products, SAR, P450 enzyme dynamics, and mechanisms. J Chem Inf Model 2012; 52: 617-48
138. Chohan KK, Paine SW, Waters NJ. Quantitative structure activity relationships in drug metabolism. Curr Top Med Chem 2006; 6: 1569-78
139. Braga R, Andrade H. QSAR and QM/MM approaches applied to drug metabolism prediction. Mini Rev Med Chem 2012; 12: 573-82
140. Maggiora GM. On outliers and activity cliffs why QSAR often disappoints. J Chem Inf Model 2006; 46: 1535-5
141. Cronin MT, Schultz TW. Pitfalls in QSAR. J Mol Struc: THEOCHEM 2003; 622: 39-51
142. Johnson SR. The trouble with QSAR (or how I learned to stop worrying and embrace fallacy). J Chem Inf Model 2008; 48: 25-6
143. Hasegawa K, Arakawa M, Funatsu K. Rational choice of bioactive conformations through use of conformation analysis and 3-way partial least squares modeling. Chemometrics Intellig Lab Syst 2000; 50: 253-61
144. Coats EA. The CoMFA steroids as a benchmark dataset for development of 3D QSAR methods. In: 3D QSAR in drug design. Springer; San Diego, CA: 1998. p. 199-213
145. Medina-Franco JL, Yongye AB, López-Vallejo F. Consensus models of activity landscapes. Stat Mod Mol Descrip QSAR/QSPR 2012; 2: 307-26
146. Cruz-Monteagudo M, Medina-Franco JL, Perez-Castillo Y, et al. Activity cliffs in drug discovery: Dr Jekyll or Mr Hyde Drug Discov Today 2014; 19: 1069-80
147. Dearden J, Cronin M, Kaiser K. How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR). SAR QSAR Environ Res 2009; 20: 241-66
148. Oprea T. 3D QSAR modeling in drug design. Comput Med Chem Drug Discov 2004; 571-616
149. Sheridan RP. Time-split cross-validation as a method for estimating the goodness of prospective prediction. J Chem Inf Model 2013; 53: 783-90
150. Netzeva TI, Worth AP, Aldenberg T, et al. Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. ATLA 2005; 33: 155-73
151. Sahigara F, Mansouri K, Ballabio D, et al. Comparison of different approaches to define the applicability domain of QSAR models. Molecules 2012; 17: 4791-810
152. Hartung T, Bremer S, Casati S, et al. A modular approach to the ECVAM principles on test validity. Altern Lab Anim 2004. 32: 467-72
153. Free SM, Wilson JW. A mathematical contribution to structure-activity studies. J Med Chem 1964; 7: 395-9
154. Dwivedi N, Mishra BN, Katoch VM. 2D-QSAR model development and analysis on variant groups of anti-tuberculosis drugs. Bioinformation 2011; 7: 82-90
155. Sharma MC, Sharma S. 2D-QSAR Study of 7-methyljuglone derivatives: an approach to design antitubercular agents. J Pharmacol Toxicol 2011; 6: 493-504
156. Sharma R, Panigrahi D, Mishra GP. QSAR studies of 7-methyljuglone derivatives as antitubercular agents. Med Chem Res 2012; 21: 2006-11
157. Marrero-Ponce Y, Martínez-Albelo ER, Casanõla-Martín GM, et al. Bond-based linear indices of the non-stochastic and stochastic edge-adjacency matrix. 1. Theory and modeling of ChemPhys properties of organic molecules. Mol Divers 2010; 14: 731-53