Industrial applications of in silico ADMET

Journal of Molecular Modeling

Volumn 20, Issue 7, 2014, Pages

  Beck, Bernd ^a   Geppert, Tim ^a  

^a BOEHRINGER INGELHEIM PHARMA GMBH AND CO KG   (Germany)

Author keywords

Automated model building; Data curation; In silico ADMET; Prediction probability; QSAR

Indexed keywords

CYTOCHROME P450 2C19; CYTOCHROME P450 2C9; CYTOCHROME P450 2D6; CYTOCHROME P450 3A4;

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER MODEL; COMPUTER PREDICTION; DRUG ABSORPTION; DRUG DISTRIBUTION; DRUG EXCRETION; DRUG METABOLISM; MACHINE LEARNING; MEASUREMENT ACCURACY; PERFORMANCE MEASUREMENT SYSTEM; PRIORITY JOURNAL; PROBABILITY; QUANTITATIVE STRUCTURE ACTIVITY RELATION; QUANTITATIVE STRUCTURE PROPERTY RELATION; RANDOM FOREST; SUPPORT VECTOR MACHINE; SURFACE PROPERTY; WORKFLOW;

EID: 84903256345     PISSN: 16102940     EISSN: 09485023     Source Type: Journal    
DOI: 10.1007/s00894-014-2322-5     Document Type: Article

References (88)

1. Richet MC (1893) Note sur le rapport entre la toxicité et les propriétés physiques des corps. C R Soc Biol 45:775-776
2. Fischer E (1894) Einfluss der Configuration auf die Wirkung der Enzyme. Ber Dtsch Chem Ges 273:2985-2993
3. Overton E (1901) Studien über die Narkose. Gustav Fischer, Jena
4. Meyer H (1899) Zur Theorie der Alkoholnarkose. Arch Exp Pathol Pharmakol 42:109-118
5. Hansch C, Maloney P, Fujita T, Muir R (1962) Correlation of biological activity of phenoxyacetic acids with Hammett substituent constants and partition coefficients. Nature 194:178-180
6. Hansch C, Sammes PG, Taylor JB (1990) Quantitative Drug Design. In: Ramsden CA (ed) Comprehensive Medicinal Chemistry. Pergamon Press, Oxford
7. Kubiniyi H (1993) QSAR. Hansch analysis and related approaches.1. In: Mangold R, Krosgaard Larsen P, Timmermann H (eds) Methods and principles in medicinal chemistry. VCH, Weinheim
8. Leo A, Jow PY, Silipo C, Hansch C (1975) Calculation of hydrophobic constant (log P) frompi and f constants. JMed Chem18:865-868
9. 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
10. Beck B, Glen RC, Clark T (1996) The inhibition of alpha-chymotrypsin predicted using theoretically derived molecular properties. J Mol Graph 14(130-5):142
11. Breindl A, Beck B, Clark T, Glen RC (1997) Prediction of the noctanol/water partition coefficient, logP, using a combination of semiempirical mo-calculations and a neural network. J Mol Model 3:142-155
12. TSAR, Oxford Molecular Limited, The Magdalen Centre, Oxford Science Park, Sandford on Thames, Oxford OX4 4GA, UK
13. van de Waterbeemd H, Gifford E (2003) ADMET in silicomodelling: towards prediction paradise? Nat Rev Drug Discov 2:192-204 (Pubitemid 37361664)
14. Kennedy T (1997) Managing the drug discovery/development interface. Drug Discov Today 210:436-444
15. Lessel U, Wellenzohn B, Lilienthal M, Claussen H (2009) Searching Fragment Spaces with Feature Trees. J Chem Inf Model 49:270-279
16. Nicolaou CA, Watson I, Wang J (2013) The Lilly Reachable Chemical Space System: bridging chemical synthesis potential with discovery chemistry, sixth joint Sheffield Conference on Chemoinformatics
17. FastROCS v1.4, OpenEye Scientific Software, Inc.: Santa Fe, NM, 2012
18. Rarey M, Dixon JS (1998) Feature trees: a new molecular similarity measure based on tree matching. J Comput Aided Mol Des 12:471-490 (Pubitemid 128512818)
19. Michel JB, Shen YK, Aiden AP, Veres A, Gray MK, Pickett JP, Hoiberg D, Clancy D, Norvig P, Orwant J, Pinker S, Nowak MA, Aiden EL (2011) Quantitative analysis of culture using millions of digitized books. Science 331:176-182
20. World Drug Index. Reuters, New York
21. Comprehensive Medicinal Chemistry, Accelrys Software Inc., San Diego
22. MDDR, Accelrys Software Inc. San Diego
23. Bolton EE, Wang Y, Thiessen PA, Bryant SH (2008) Chapter 12 PubChem: integrated platform of small molecules and biological activities. In: Ralph AWaD, (ed) Annual reports in computational chemistry, Elsevier, pp. 217-241.
24. Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100-D1107
25. Chambers J, Davies M, Gaulton A, Hersey A, Velankar S, Petryszak R, Hastings J, Bellis L, McGlinchey S, Overington J (2013) UniChem: a unified chemical structure cross-referencing and identifier tracking system. J Chem Inf 5:3
26. Integrity, Thomson Reuters, New York
27. Young D, Martin T, Venkatapathy R, Harten P (2008) Are the chemical structures in your QSAR correct? QSAR Comb Sci 27:1337-1345
28. Fourches D, Muratov E, Tropsha A (2010) Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J Chem Inf Model 50:1189-1204
29. Olah MM, Bologa CG, Oprea TI (2004) Strategies for compound selection. Curr Drug Discov Technol 1:211-220
30. Tiikkainen P, Franke L (2011) Analysis of commercial and public bioactivity databases. J Chem Inf Model 52:319-326
31. Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inf 29:476-488
32. Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuzmin VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A (2013) QSAR modeling: where have you been? Where are you going to? J Med Chem. doi:10.1021/jm4004285
33. Todeschini R and Consonni V (2000) Frontmatter. In: Handbook of molecular descriptors. Wiley-VCH, Weinheim, pp i-xxi
34. Dragon 6, TALETE srl, Via V. Pisani, 13 - 20124 Milano - Italy, 2013
35. Kramer C & Clark T (2012) New types of descriptors and models in QSAR/QSPR. In: Statistical modelling of molecular descriptors in QSAR/QSPR. Wiley-VCH, Weinheim, pp 293-305
36. Hopfinger AJ (1980) A QSAR investigation of dihydrofolate reductase inhibition by Baker triazines based upon molecular shape analysis. J Am Chem Soc 102:7196-7206
37. Hopfinger AJ (1981) Inhibition of dihydrofolate reductase: structure-activity correlations of 2,4-diamino-5-benzylpyrimidines based upon molecular shape analysis. J Med Chem 24:818-822
38. Hopfinger AJ, Wang S, Tokarski JS, Jin B, Albuquerque M, Madhav PJ, Duraiswami C (1997) Construction of 3D-QSAR models using the 4D-QSAR analysis formalism. J Am Chem Soc 119:10509-10524 (Pubitemid 27487990)
39. Hopfinger AJ, Reaka A, Venkatarangan P, Duca JS, Wang S (1999) Construction of a virtual high throughput screen by 4D-QSAR analysis: application to a combinatorial library of glucose inhibitors of glycogen phosphorylase b. J Chem Inf Comput Sci 39:1151-1160 (Pubitemid 129579851)
40. Kier LB, Hall LH (1976) Molecular connectivity in chemistry and drug research. Academic, New York
41. Beck B, Horn A, Carpenter JE, Clark T (1998) Enhanced 3D-databases: a fully electrostatic database of AM1-optimized structures. J Chem Inf Comput Sci 38:1214-1217 (Pubitemid 128591287)
42. Beck B, Breindl A, Clark T (2000) QM/NN QSPR models with error estimation: vapor pressure and logP. J Chem Inf Comput Sci 40:1046-1051
43. Brüstle M, Beck B, Schindler T, King W, Mitchell T, Clark T (2002) Descriptors, physical properties, and drug-likeness. J Med Chem 45:3345-3355 (Pubitemid 34824024)
44. Ehresmann B, de Groot MJ, Clark T (2005) Surface-integral QSPR models: local energy properties. J Chem Inf Model 45:1053-1060 (Pubitemid 41156706)
45. Kramer C, Beck B, Kriegl J, Clark T (2008) A composite model for hERG blockade. Chem Med Chem 3:254-265
46. Hennemann M, Friedl A, Lobell M, Keldenich J, Hillisch A, Clark T, Göller A (2009) CypScore: quantitative prediction of reactivity toward cytochromes P450 based on semiempirical molecular orbital theory. Chem Med Chem 4:657-669
47. Kramer C, Beck B, Clark T (2010) A Surface-Integral Model for Log POW. J Chem Inf Model 50:429-436
48. Shahlaei M (2013) Descriptor selection methods in quantitative structure activity relationship studies: a review study. Chem Rev 113:8093-8103
49. Breiman L (2001) Random Forests. Mach Learn 5-32
50. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273-297
51. Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65:386-408
52. Legendre AM (1806) Nouvelles méthodes pour la détermination des orbites des comètes: avec un supplément contenant divers perfectionnements perfectionnements. de ces méthodes et leur application aux deux comètes de 1805. Courcier, Paris
53. Gauss KF (1857) Theory of the motion of the heavenly bodies moving about the sun in conic sections. Dover, Phoenix
54. Livingstone DJ (2000) The characterization of chemical structures using molecular properties. A survey. J Chem Inf Comput Sci 40:195-209
55. Tarca AL, Carey VJ, Xw C, Romero R, Drâghici S (2007) Machine learning and its applications to biology. PLoS Comput Biol 3:e116
56. Rasmussen CE (2004) Gaussian processes in machine learning. Lect Notes Comput Sci 3176:63-71
57. Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43:59-69
58. Schneider P, Stutz K, Kasper L, Haller S, Reutlinger M, Reisen F, Geppert T, Schneider G (2011) Target profile prediction and practical evaluation of a Biginelli-type dihydropyrimidine compound library. Pharmaceuticals 4:1236-1247
59. Tropsha A, Gramatica P, Gombar V (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69-77 (Pubitemid 36717473)
60. McNemar Q (1947) Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12:153-157
61. Cumming JG, Davis AM, Muresan S, Haeberlein M, Chen H (2013) Chemical predictive modelling to improve compound quality. Nat Rev Drug Discov 12:948-962
62. ChemAxon (2013) J Chem 5.11.5 http://www.chemaxon.com
63. Molecular Operating Environment (MOE), 2013.08; Chemical Computing Group Inc., 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2013
64. CORINA - Generation of 3D coordinates, version 3.0, Molecular Networks GmbH, Erlangen, Germany
65. Deng H, Runger G (2013) Gene selection with guided regularized random forest. Pattern Recogn 46:3483-3489
66. Friedman JH, Hastie T, Tibshirani R (2008) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33:1-22
67. Schneider G, Neidhart W, Giller T, Schmid G (1999) Scaffold-hopping by topological pharmacophore search: a contribution to virtual screening. Angew Chem Int Ed 38:2894-2896 (Pubitemid 29479958)
68. Berthold MR, Cebron M, Dill F, Gabriel TR, Kötter T, Meinl T, Ohl P, Sieb C, Thiel K, Wiswedel B (2007) KNIME: the konstanz information miner. In:Studies in classification, data analysis, and knowledge organization. Springer, Heidelberg
69. Pipeline Pilot, Version 8.0. Accelrys Software Inc. San Diego: s.n.; 2011
70. R Core Team (2013) R: A Language and Environment for statistical computing. R Foundation for Statistical Computing. (http://www.R-project.org)
71. Kriegl JM, Arnhold T, Beck B, Fox T (2005) A support vector machine approach to classify human cytochrome P450 3A4 inhibitors. J Comput Aided Mol Des 19:189-201 (Pubitemid 40974006)
72. Veith H, Southall N, Huang R, James T, Fayne D, Artemenko N, Shen M, Inglese J, Austin CP, Lloyd DG, Auld DS (2009) Comprehensive characterization of cytochrome P450 isozyme selectivity across chemical libraries. Nat Biotechnol 27:1050-1055
73. Cheng F, Yu Y, Shen J, Yang L, Li W, Liu G, Lee PW, Tang Y (2011) Classification of cytochrome P450 inhibitors and noninhibitors using combined classifiers. J Chem Inf Model 51:996-1011
74. Sun H, Veith H, Xia M, Austin CP, Huang R (2011) Predictive models for cytochrome P450 isozymes based on quantitative high throughput screening data. J Chem Inf Model 51:2474-2481
75. OECD (2005) Principles for the validation, for regulatory purposes, of (quantitative) structure-activity relationship models
76. OPS (2000) TOPKAT OPS. US patent no. 6 036 349
77. Sheridan R, Feuston RP, Maiorov VN, Kearsley S (2004) Similarity to molecules in the training set is a good discriminator for prediction accuracy in QSAR. J Chem Inf Comput Sci 44:1912-1928
78. Sahigara F, Ballabio D, Todeschini R, Consonni V (2013) Defining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions. J Chem Inf 5:27
79. Netzeva TI, Worth A, Aldenberg T, Benigni R, Cronin MT, Gramatica P, Jaworska JS, Kahn S, Klopman G, Marchant CA, Myatt G, Nikolova-Jeliazkova N, Patlewicz GY, Perkins R, Roberts D, Schultz T, Stanton DW, van de Sandt JJ, Tong W, Veith G, Yang C (2005) Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52. Altern Lab Anim 33:155-173 (Pubitemid 40686385)
80. Jaworska J, Nikolova-Jeliazkova N, Aldenberg T (2005) QSAR applicabilty domain estimation by projection of the training set descriptor space: a review. Altern Lab Anim 33:445-459 (Pubitemid 41613738)
81. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab 2:37-52
82. Worth AP, Bassan A, Gallegos A, Netzeva TI, Patlewicz G, Pavan M, Tsakovska I, Vra-ìko M (2005) The Characterisation of (quantitative) structure-activity relationships: preliminary guidance. Institute for Health and Consumer Protection, Toxicology and Chemical Substances Unit, European Chemical Bureau
83. Sahigara F, Mansouri K, Ballabio D, Mauri A, Consonni V, Todeschini R (2012) Comparison of different approaches to define the applicability domain of QSAR models. Molecules 17:4791-4810
84. Clark RD, Liang W, Waldman M, Fraczkiewicz R (2013) Estimating classification confidence for ensemble models. Sixth Joint Sheffield Conference on Chemoinformatics
85. Pence HE, Williams A (2010) ChemSpider: an online chemical information resource. J Chem Educ 87:1123-1124
86. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46:3-26 (Pubitemid 33653411)
87. Duewer DL (1990) The free - Wilson paradigmredux: Significance of the free - Wilson coefficients, insignificance of coefficient 'uncertainities' and statistical sins. J Chemometr 4:299-321
88. Stardrop, Optibrium Ltd., Cambridge Research, Cambridge, UK