Classification models for safe drug molecules

Methods in Molecular Biology

Volumn 930, Issue , 2013, Pages 99-124

  Madan, A K ^a   Bajaj, Sanjay ^b   Dureja, Harish ^c  

^a PT B D SHARMA UNIVERSITY OF HEALTH SCIENCES   (India)

^b SV College of Pharmacy   (India)

^c MAHARSHI DAYANAND UNIVERSITY   (India)

Author keywords

Bioavailability; Biological activity; Classification models; Drug safety; QSAR models; Toxicity

Indexed keywords

DRUG;

ANIMAL; ARTICLE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; CLASSIFICATION; CLUSTER ANALYSIS; DECISION TREE; DISCRIMINANT ANALYSIS; FACTORIAL ANALYSIS; HUMAN; PRINCIPAL COMPONENT ANALYSIS; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SUPPORT VECTOR MACHINE; WORKFLOW;

ANIMALS; BAYES THEOREM; CLUSTER ANALYSIS; DECISION TREES; DISCRIMINANT ANALYSIS; FACTOR ANALYSIS, STATISTICAL; HUMANS; PATTERN RECOGNITION, AUTOMATED; PHARMACEUTICAL PREPARATIONS; PRINCIPAL COMPONENT ANALYSIS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SUPPORT VECTOR MACHINES; WORKFLOW;

ANIMALIA;

EID: 84870497766     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-62703-059-5_5     Document Type: Article

References (117)

1. Sneyd JR (2001) Drug development in 21st century. Curr Anaesth Crit Care 12:329-334
2. Kubinyi H (2003) Drug research: Myths, hype and reality. Nat Rev Drug Discov 2: 665-668
3. Sussman NL, Kelly JH (2003) Saving time and money in drug discovery-A pre-emptive approach. Business Briefing. Future Drug Discov. Business Briefing Ltd., London, UK, 46-49
4. DiMasi J, Hansen R, Grabowski H (2003) The price of innovation: New estimates of drug development cost. J Health Econ 22: 151-185
5. Marjana N, Marjana V (2010) QSAR model for reproductive toxicity and endocrine disruption activity. Molecules 15:1987-1999
6. Chen JW, Li XH, Yu HYet al (2008) Progress and perspectives of quantitative structureactivity relationships used for ecological risk assessment of toxic organic compounds. Sci China Ser B Chem 51:593-606
7. Ibezim EC, Duchowicz PR, Ibezim NE et al (2009) Computer-aided linear modeling employing QSAR for drug discovery. Sci Res Essays 4:1559-1564
8. Johnson DE, Wolfgang GHI (2000) Predicting human safety: Screening and computational approaches. Drug Discov Today 5: 445-454
9. Hou T, Wang J, Zhang Wet al (2006) Recent advances in computational prediction of drug absorption and permeability in drug discovery. Curr Med Chem 13:2653-2667
10. Thai KM, Ecker GF (2008) A binary QSAR model for classification of hERG potassium channel blockers. Bioorg Med Chem 16: 4107-4119
11. Eitrich T, Kless A, Druska C et al (2007) Classification of highly unbalanced cyp450 data of drugs using cost sensitive machine learning techniques. J Chem Inf Model 47: 92-103
12. Ekins S, Boulanger B, Swaan PW et al (2002) Towards a new age of virtual ADME/TOX and multidimensional drug discovery. J Comput Aided Mol Design 16:381-401
13. Harvey SC (1980) Drug absorption, action and disposition. In: Osol A (ed) Remington's pharmaceutical sciences, 16th edn. Mack, Pennsylvania
14. Mandell GL, Petri WA Jr (1996) Antimicrobial agents: Penicillins, cephalosporins, and other b-lactam antibiotics. In: Hardman JG, Limbird LE (eds) Goodman and Gilman's the pharmacological basis of therapeutics, 9th edn. McGraw-Hill, New York
15. Tsantrizos YS (2010) Research and development-the discovery process http://www. wei-c-chen.com/winter2010/chem503/04% 20-%20ADME- PK%20drug%20delivery.pdf. Accessed 4 Dec 2010
16. Bajorath J (2001) Selected concepts and investigation in compound classification, molecular descriptor analysis and virtual screening. J Chem Inf Comput Sci 41: 233-245
17. Bassan A, Worth AP (2008) The integrated use of models for the properties and effects of chemicals by means of a structured workflow. QSAR Comb Sci 27:6-20
18. Worth AP, Cronin MTD (2003) The use of discriminant analysis, logistic regression and classification tree analysis in the development of classification models for human health effects. J Mol Str Theochem 622:97-111
19. OECD (2007), Guidance document on the validation of (Quantitative) Structure-Activity Relationships [(Q)SAR] Models, p. 62, OECD Environment Health and Safety Publications Series on Testing and Assessment, No. 69
20. Grover M, Singh B, Bakshi M, Singh S (2000) Quantitative structure-property relationships in pharmaceutical research. Part 1. Pharm Sci Technol Today 3:28-35
21. Gupta S, Singh M, Madan AK (2001) Predicting anti-HIV activity: Computational approach using a novel topological descriptor. J Comput Aided Mol Des 15:671-678
22. Worth AP, Cronin MTD (2000) Embedded cluster modelling: A novel quantitative structure-activity relationship for generating elliptic models of biological activity. In: Balls M, van Zeller AM, Halder ME (eds) Progress in the reduction, refinement and replacement of animal experimentation. Elsevier Science, Amsterdam
23. Frimurer TM, Bywater R, Naerum L (2000) Improving the odds in discriminating "druglike" from "non drug-like" compounds. J Chem Inf Comput Sci 40:1315-1324
24. Weber KC, HonArio KM, Bruni AT et al (2006) The use of classification methods for modeling the antioxidant activity of flavonoid compounds. J Mol Model 12:915-920
25. Gillet VJ, Willett P, Bradshaw J (1998) Identification of biological activity profiles using substructural analysis and genetic algorithms. J Chem Inf Comput Sci 38:165-179
26. Alvarez-Ginarte YM, Crespo-Otero R, Marrero-Poncec Y et al (2006) In-silico classification of solubility using binary k-Nearest Neighbour and physicochemical descriptors. QSAR Comb Sci 25:881-894
27. Rodgers AD, Zhu H, Fourches D et al (2010) Modeling liver-related adverse effects of drugs using k nearest neighbor quantitative structure-activity relationship method. Chem Res Toxicol 23:724-732
28. Khlebnikov AI, Schepetkin IA, Kirpotina LN et al (2008) Computational structure-activity relationship analysis of non-peptide inducers of macrophage tumor necrosis factor-alpha production. Bioorg Med Chem 16:9302-9312
29. Nisius B, Goller AH, Bajorath J (2009) Combining cluster analysis, feature selection and multiple support vector machine models for the identification of human ether-a-go-go related gene channel blocking compounds. Chem Biol Drug Des 73:17-25
30. Kauffman GW, Jurs PC (2001) QSAR and k-Nearest Neighbour Classification Analysis of selective cyclooxygenase-2 inhibition using topologically-based numerical descriptors. J Chem Inf Comput Sci 4:1553-1560
31. Efferth T, Konkimalla VB, Wang YF et al (2008) Prediction of broad spectrum resistance of tumors towards anticancer drugs. Clin Cancer Res 14:2405-2412
32. Casanola-Martín GM, Marrero-Ponce Y, Khan MTH et al (2008) Atom-and bondbased 2D TOMOCOMD-CARDD approach and ligand-based virtual screening for the drug discovery of new tyrosinase inhibitors. J Biomol Screen 13:1014-1024
33. Lv W, Xue Y (2010) Prediction of acetylcholinesterase inhibitors and characterization of correlative molecular descriptors by machine learning methods. Eur J Med Chem 45: 1167-1172
34. Boiani M, Cerecetto H, Gonzalez M et al (2008) Modeling anti-Trypanosoma cruzi activity of N-oxide containing heterocycles. J Chem Inf Model 48:213-219
35. Lin HH, Han LY, Yap CW et al (2007) Prediction of factor Xa inhibitors by machine learning methods. J Mol Graph Model 26:505-518
36. Gunturi SB, Theerthala SS, Patel NK et al (2010) Prediction of skin sensitization potential using D-optimal design and GA-kNN classification methods. SAR QSAR Environ Res 21:305-335
37. Polishchuk PG, Muratov EN, Artemenko AG et al (2009) Application of random forest approach to QSAR prediction of aquatic toxicity. J Chem Inf Model 49:2481-2488
38. Zhu H, Rusyn I, Richard A et al (2008) Use of cell viability assay data improves the prediction accuracy of conventional quantitative structure-activity relationship models of animal carcinogenicity. Environ Health Perspect 116:506-513
39. Zhang S, Wei L, Bastow K et al (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
40. Asikainen A, Kolehmainen M, Ruuskanen J et al (2006) Structure-based classification of active and inactive estrogenic compounds by decision tree, LVQ and kNN methods. Chemosphere 62:658-673
41. Kadam RU, Roy N (2006) Cluster analysis and two-dimensional quantitative structureactivity relationship (2D-QSAR) of Pseudomonas aeruginosa deacetylase LpxC inhibitors. Bioorg Med Chem Lett 16:5136-5143
42. Hammann F, Gutmann H, Baumann U et al (2009) Classification of cytochrome p(450) activities using machine learning methods. Mol Pharm 6:1920-1926
43. Petkov PI, Temelkov S, Villeneuve DL et al (2009) Mechanism-based categorization of aromatase inhibitors: A potential discovery and screening tool. SAR QSAR Environ Res 20:657-678
44. Burton J, Danloy E, Vercauteren DP (2009) Fragment-based prediction of cytochromes P450 2D6 and 1A2 inhibition by recursive partitioning. SAR QSAR Environ Res 20: 185-205
45. Hammann F, Gutmann H, Jecklin U et al (2009) Development of decision tree models for substrates, inhibitors, and inducers of pglycoprotein. Curr Drug Metab 10:339-346
46. Yang XG, Chen D, Wang M et al (2009) Prediction of antibacterial compounds by machine learning approaches. J Comput Chem 30:1202-1211
47. Kuzmin VE, Polischuk PG, Artemenko AG et al (2008) Quantitative structure-affinity relationship of 5-HT1A receptor ligands by the classification tree method. SAR QSAR Environ Res 19:213-244
48. Panaye A, Doucet JP, Devillers J et al (2008) Decision trees versus support vector machine for classification of androgen receptor ligands. SAR QSAR Environ Res 19:129-151
49. Liao Q, Yao J, Yuan S (2007) Prediction of mutagenic toxicity by combination of Recursive Partitioning and Support Vector Machines. Mol Divers 11:59-72
50. Kim HJ, Choo H, Cho YS et al (2006) Classification of dopamine, serotonin, and dual antagonists by decision trees. Bioorg Med Chem 14:2763-2770
51. Hong H, Tong W, Xie Q (2005) An in silico ensemble method for lead discovery: Decision forest. SAR QSAR Environ Res 16:339-347
52. Dureja H, Gupta S, Madan AK (2009) Topological models for prediction of physicochemical, pharmacokinetic and toxicological properties of antihistaminic drugs using decision tree and moving average analysis. Int J Comput Biol Drug Des 2:353-370
53. Goyal RK, Dureja H, Singh G, Madan AK (2010) Models for antitubercular activity of 50-O-[(N-Acyl)sulfamoyl]adenosines. Sci Pharm 78:791-820
54. Roy K, Sanyal I (2005) QSTR with extended topochemical atom indices. 7. QSAR of substituted benzenes to Saccharomyces cerevisiae. QSAR Comb Sci 25:359-371
55. Bhatia MS, Ingale KB, Choudhari PB et al (2008) Application quantum and physico chemical molecular descriptors utilizing principal components to study mode of anticoagulant activity of pyridyl chromen-2-one derivatives. BioorgMed Chem 17:1654-1662
56. HonArio KM, da Silva AB (2005) A study on the influence of molecular properties in the psychoactivity of cannabinoid compounds. J Mol Model 11:200-209
57. Molfetta FA, Bruni AT, HonArio KM et al (2005) A structure-activity relationship study of quinone compounds with trypanocidal activity. Eur J Med Chem 40:329-338
58. Warne MA, Nicholson JK, Lindon JC et al (2009) A QSAR investigation of dermal and respiratory chemical sensitizers based on computational chemistry properties. SAR QSAR Environ Res 20:429-451
59. De O, Figueiredo LJ, Garrido FM, Kunisawa VY et al (2006) A chemometric study of phosphodiesterase 5 inhibitors. J Mol Graph Model 24:227-232
60. Lameira J, Medeiros IG, Reis M et al (2006) Structure-activity relationship study of flavones compounds with anti-HIV-1 integrase activity: A density functional theory study. Bioorg Med Chem 14:7105-7112
61. de Melo LC, Braga SF, Barone PM (2007) Pattern recognition methods investigation of ellipticines structure-activity relationships. J Mol Graph Model 25:912-920
62. Perez-Garrido A, Helguera AM, Rodriguez FG et al (2010) QSAR models to predict mutagenicity of acrylates, methacrylates and alpha, beta-unsaturated carbonyl compounds. Dent Mater 26:397-415
63. Casanola-Martin GM, Marrero-Ponce Y, Khan MT et al (2007) Dragon method for finding novel tyrosinase inhibitors: Biosilico identification and experimental in vitro assays. Eur J Med Chem 42:1370-1381
64. Garcia I, Fall Y, Gaimez G et al (2010) 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. doi:10.1007/s11030-010-9280-3
65. Le-Thi-Thu H, Casanola-Martin GM, Marrero-Ponce Y et al (2010) Novel coumarin-based tyrosinase inhibitors discovered by OECD principles-validated QSAR approach from an enlarged, balanced database. Mol Divers. doi:10.1007/s11030-010-9274-1
66. Vilar S, Chakrabarti M, Costanzi S (2010) Prediction of passive blood-brain partitioning: Straightforward and effective classification models based on in silico derived physicochemical descriptors. J Mol Graph Model 28:899-903
67. Prado-Prado FJ, Garcaa-Mera X, Gonzalez-Daaz H (2010) Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. Bioorg Med Chem 18:2225-2231
68. Castillo-Garit JA, Vega MC, Rolon M et al (2010) Computational discovery of novel trypanosomicidal drug-like chemicals by using bond-based non-stochastic and stochastic quadratic maps and linear discriminant analysis. Eur J Pharm Sci 39:30-36
69. Prado-Prado FJ, Ubeira FM, Borges F et al (2010) Unified QSAR & network-based computational chemistry approach to antimicrobials. II. Multiple distance and triadic census analysis of antiparasitic drugs complex networks. J Comput Chem 31:164-173
70. Prado-Prado FJ, Borges F, Perez-Montoto LG et al (2009) Multi-target spectral moment: QSAR for antifungal drugs vs. different fungi species. Eur J Med Chem 44:4051-4056
71. Sun M, Chen J, Wei H et al (2009) Quantitative structure-activity relationship and classification analysis of diaryl ureas against vascular endothelial growth factor receptor-2 kinase using linear and-linear models. Chem Biol Drug Des 73:644-654
72. Gozalbes R, Barbosa F, Nicola E et al (2009) Development and validation of a pharmacophore-based QSAR model for the prediction of CNS activity. ChemMedChem 4:204-209
73. Garcia-Domenech R, Lopez-Pena W, Sanchez-Perdomo Y et al (2008) Application of molecular topology to the prediction of the antimalarial activity of a group of uracil-based acyclic and deoxyuridine compounds. Int J Pharm 363:78-84
74. Castillo-Garit JA, Marrero-Ponce Y, Torrens F et al (2008) Estimation of ADME properties in drug discovery: Predicting Caco-2 cell permeability using atom-based stochastic and non-stochastic linear indices. J Pharm Sci 97:1946-1976
75. Alvarez-Ginarte YM, Marrero-Ponce Y, Ruiz-Garcia JA (2008) Applying pattern recognition methods plus quantum and physicochemical molecular descriptors to analyze the anabolic activity of structurally diverse steroids. J Comput Chem 29:317-333
76. Kamphorst J, Cucurull-Sanchez L, Jones B (2007) A performance evaluation of multiple classification models of human PEPT1 inhibitors and non-inhibitors. QSAR Comb Sci 26:220-226
77. Cannon EO, Amini A, Bender A et al (2007) Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds. J Comput Aided Mol Des 21:269-280
78. Sun H (2006) An accurate and interpretable bayesian classification model for prediction of HERG liability. ChemMedChem 1:315-322
79. Wang J, Liu H, Qin S et al (2007) Study on structure-activity relationship of new anti-HIV nucleoside derivatives based on the support vector machine method. QSAR Comb Sci 26:161-172
80. Sato T, Matsuo Y, Honma T (2008) In silico functional profiling of small molecules and its applications. J Med Chem 51:7705-7716
81. Du H, Wang J, Watzl J et al (2008) Classification structure-activity relationship (CSAR) studies for prediction of genotoxicity of thiophene derivatives. Toxicol Lett 177:10-19
82. Tang H, Wang XS, Huang XP (2009) Novel inhibitors of human histone deacetylase (HDAC) identified by QSAR modeling of known inhibitors, virtual screening, and experimental validation. J Chem Inf Model 49:461-476
83. Leong MK, Chen TH (2008) Prediction of cytochrome P450 2B6-substrate interactions using pharmacophore ensemble/support vector machine (PhE/SVM) approach. Med Chem 4:396-406
84. Leong MK (2007) A novel approach using pharmacophore ensemble/support vector machine (PhE/SVM) for prediction of hERG liability. Chem Res Toxicol 20:217-226
85. Auerbach SS, Shah RR, Mav D et al (2010) Predicting the hepatocarcinogenic potential of alkenylbenzene flavoring agents using toxicogenomics and machine learning. Toxicol Appl Pharmacol 243:300-314
86. Leong MK, Chen YM, Chen HB et al (2009) Development of a new predictive model for interactions with human cytochrome P450 2A6 using pharmacophore ensemble/support vector machine (PhE/SVM) approach. Pharm Res 26:987-1000
87. Votano JR, Parham M, Hall LM (2006) QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation. J Med Chem 49:7169-7181
88. Fernandez M, Caballero J (2006) Ensembles of Bayesian-regularized genetic neural networks for modeling of acetylcholinesterase inhibition by huprines. Chem Biol Drug Des 68:201-212
89. Tarasov VA, Mustafaev ON, Abilev SK (2005) Use of ensemble structural descriptors for increasing the efficiency of QSAR study. Genetika 41:997-1005
90. Guha R, Jurs PC (2004) Development of linear, ensemble, and nonlinear models for the prediction and interpretation of the biological activity of a set of PDGFR inhibitors. J Chem Inf Comput Sci 44:2179-2189
91. Debeljak Z, Skrbo A, Jasprica I et al (2007) QSAR study of antimicrobial activity of some 3-nitrocoumarins and related compounds. J Chem Inf Model 47:918-926
92. Bajaj S, Sambi SS,Madan AK (2004) Prediction of carbonic anhydrase activation of tri/tetra substituted-pyridinium-azole compounds-Computational approach using novel topochemical descriptor. QSAR Comb Sci 23:506-514
93. Bajaj S, Sambi SS, Madan AK (2004) Topological models for prediction of antiinflammatory activity of N-arylanthranilic acids. Bioorg Med Chem 12:3695-3701
94. Bajaj S, Sambi SS, Madan AK (2005) Topochemical model for prediction of corticotropin releasing factor antagonizing activity of N-phenylphenylglycines analogs. Acta Chim Slov 52:292-296
95. Bajaj S, Sambi SS, Madan AK (2008) Topochemical models for predicting the activity of a, g-diketo acids as inhibitors of the hepatitis C virus NS5B RNA-dependent RNA polymerase. Pharmaceut Chem J 40:650-654
96. Sardana S, Madan AK (2002) Predicting anticonvulsant activity of benzamides/benzylamines: Computational approach using topological descriptors. J Comput Aided Mol Des 16:545-550
97. Lather V, Madan AK (2005) Topological model for the prediction of MRP1 inhibitory activity of pyrrolopyrimidines and templates derived from pyrrolopyrimidine. Bioorg Med Chem Lett 15:4967-4972
98. Gupta S, Singh M, Madan AK (1999) Superpendentic index: A novel topological descriptor for predicting biological activity. J Chem Inf Comput Sci 39:272-277
99. Kumar V, Madan AK (2006) Application of graph theory: Prediction of cytosolic phospholipase A2 inhibitory activity of propan-2-ones. J Math Chem 39:511-521
100. Kumar V, Madan AK (2005) Application of graph theory: Prediction of glycogen synthase kinase-3 inhibitory activity of thiadiazolidinones as potential drugs for the treatment of Alzheimer's disease. Eur J Pharm Sci 24:213-218
101. Kumar V, Madan AK (2007) Application of graph theory: Models for prediction of carbonic anhydrase inhibitory activity of sulphonamides. J Math Chem 42:925-940
102. Dureja H, Madan AK (2005) Topochemical models for prediction of cyclin-dependent kinase 2 inhibitory activity of indole-2-ones. J Mol Model 11:525-531
103. Dureja H, Madan AK (2006) Prediction of h5-HT2A receptor antagonistic activity of arylindoles: Computational approach using topochemical descriptors. J Mol Graph Model 25:373-379
104. Dureja H, Madan AK (2007) Topochemical models for prediction of telomerase inhibitory activity of flavonoids. Chem Biol Drug Des 70:47-52
105. Bajaj S, Sambi SS, Madan AK (2006) Models for prediction of anti-neoplastic activity of 1,2-Bis(sulfonyl)-1-methylhydrazines: Computational approach using Wiener's indices. MATCH Commun Math Comput Chem 56:193-204
106. Blower PE, Cross KP (2006) Decision tree methods in pharmaceutical research. Curr Top Med Chem 6:31-39
107. Andres C, Hutter MC (2006) CNS permeability of drugs predicted by a decision tree. QSAR Comb Sci 25:305-309
108. Dudek AZ, Arodz T, Galvez J (2006) Computational methods in developing quantitative structure-activity relationship: A review. Comb Chem High Throughput Screen 9:213-228
109. Breiman L, Friedman JH, Olshen RA et al (1984) Classification and regression trees. Wadsworth International Group, Belmont
110. Svetnik V, Liaw A, Tong C et al (2003) Random forest: A classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43:1947-1958
111. Tong W, Hong H, Fang H et al (2003) Decision forest: Combining the predictions of multiple independent decision tree models. J Chem Inf Comput Sci 43:525-531
112. Aboul-Kassim TAT, Simoneit BRT (2001) QSAR/QSPR and multicomponent joint toxic effect modeling of organic pollutants at aqueous-solid phase interfaces, vol 5 E, The handbook of environmental chemistry. Springer, Heidelberg
113. Wold S, DunntWJ,Hellberg S (1985) Toxicity modeling and prediction with pattern recognition. Environ Health Perspect 61:257-268
114. http://www-users.cs.umn.edu/~kumar/ dmbook/ch8.pdf. Accessed 15 Aug 2010
115. http://www.geosoft.com. Accessed 28 Nov 2010
116. Jalali-Heravi M, Shahbazikhah P, Zekavat B et al (2007) Principal component analysisranking as a variable selection method for the simulation of 13C nuclear magnetic resonance spectra of xanthones using artificial neural networks. QSAR Comb Sci 26:764-772
117. Adenot M, Perriere N, Scherrmann JM et al (2007) Applications of a blood-brain barrier technology platform to predict CNS penetration of various chemotherapeutic agents. 1. Anti-infective drugs. Chemotherapy 53:70-72