Data Mining Approaches for Compound Selection and Iterative Screening

Pharmaceutical Data Mining: Approaches and Applications for Drug Discovery

Volumn , Issue , 2009, Pages 113-143

  Vogt, Martin ^a   Bajorath, Jürgen ^a  

^a UNIVERSITY OF BONN   (Germany)

Author keywords

Bayesian modeling and Bayesian na ve classifiers for applications in virtual screening; Chemoinformatics based applications; Data mining approaches for compound selection and iterative screening

Indexed keywords

EID: 77955243058     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470567623.ch4     Document Type: Chapter

References (94)

1. Halperin I. Ma B. Wolfson H. Nussinov R. Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins 2002 ; 47 : 409-443.
2. Klebe G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 2006 ; 11 : 580-594.
3. Mason JS. Good AC. Martin EJ. 3 - D pharmacophores in drug discovery. Curr Pharm Des 2006 ; 7 : 567-597.
4. Hawkins P. Skillman A. Nicholls A. Comparison of shape - matching and docking as virtual screening tools. J Med Chem 2007 ; 50 : 74-82.
5. Sheridan RP. Kearsley SK. Why do we need so many chemical similarity search methods? Drug Discov Today 2002 ; 7 : 903-911.
6. McGaughey GB. Sheridan RP. Bayly CI. Culberson JC. Kreatsoula C. Lindsley S. Maiorov V. Truchon JF. Cornell WD. Comparison of topological, shape, and docking methods in virtual screening. J Chem Inf Model 2007 ; 47 : 1504-1519.
7. Lipinski CA. Lombardo F. Dominy BW. Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 1997 ; 23 : 3-25.
8. Willett P. Winterman V. Bawden D. Implementation of nonhierarchic cluster analysis methods in chemical information systems: Selection of compounds for biological testing and clustering of substructure search output. J Chem Inf Comput Sci 1986 ; 26 : 109-118.
9. Barnard JM. Downs GM. Clustering of chemical structures on the basis of two -dimensional similarity measures. J Chem Inf Comput Sci 1992 ; 32 : 644-649.
10. Brown RD. Martin YC. Use of structure - activity data to compare structure - based clustering methods and descriptors for use in compound selection. J Chem Inf Comput Sci 1996 ; 36 : 572-584.
11. Brown RD. Martin YC. The information content of 2D and 3D structural descriptors relevant to ligand - receptor binding. J Chem Inf Comput Sci 1997 ; 37 : 1-9.
12. Pearlman RS. Smith K. Novel software tools for chemical diversity. Perspect Drug Discov Des 1998 ; 9 : 339-353.
13. Bajorath J. Integration of virtual and high - throughput screening. Nat Rev Drug Discov 2002 ; 1 : 882-894.
14. Schnur D. Beno BR. Good A. Tebben A. Approaches to Target Class Combinatorial Library Design. Chemoinformatics - Concepts, Methods, and Tools for Drug Discovery. pp. 355-378. Totowa, NJ : Humana Press. 2004.
15. Todeschini R. Consonni V. Handbook of Molecular Descriptors. Weinheim : Wiley - VCH. 2000.
16. Maldonado AG. Doucet JP. Petitjean M. Fan BT. Molecular similarity and diversity in chemoinformatics: From theory to applications. Mol Divers 2006 ; 10 : 39-79.
17. Weininger D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comp Sci 1988 ; 28 : 31-36.
18. Weininger D. Weininger A. Weininger JL. SMILES. 2. Algorithm for generation of unique smiles notation. J Chem Inf Comput Sci 1989 ; 29 : 97-101.
19. Stein SE. Heller SR. Tchekhovski D. An open standard for chemical structure representation - The IUPAC chemical identifi er. In Nimes International Chemical Information Conference Proceedings. edited by Collier H. pp. 131-143.Tetbury, UK : Infomatics. 2002. Available at http://www.iupac.org/inchi/ (accessed February 1, 2008 ).
20. Vidal D. Thormann M. Pons M. LINGO, an effi cient holographic text based method to calculate biophysical properties and intermolecular similarities. J Chem Inf Model 2005 ; 45 : 386-393.
21. Grant J. Haigh J. Pickup B. Nicholls A. Sayle R. Lingos, fi nite state machines, and fast similarity searching. J Chem Inf Model 2006 ; 46 : 1912-1918.
22. Azencott CA. Ksikes A. Swamidass SJ. Chen JH. Ralaivola L. Baldi P. One - to four - dimensional kernels for virtual screening and the prediction of physical, chemical, and biological properties. J Chem Inf Model 2007 ; 47 : 965-974.
23. Gillet V. Willett P. Bradshaw J. Similarity searching using reduced graphs. J Chem Inf Comput Sci 2003 ; 43. 338-345.
24. Labute P. Derivation and Applications of Molecular Descriptors Based on Approximate Surface Area. Chemoinformatics - Concepts, Methods, and Tools for Drug Discovery. pp. 261-278. Totowa, NJ : Humana Press. 2004.
25. Barnard JM. Substructure searching methods: Old and new. J Chem Inf Comput Sci 1993 ; 33 : 532-538.
26. Raymond J. Gardiner E. Willett P. Heuristics for similarity searching of chemical graphs using a maximum common edge subgraph algorithm. J Chem Inf Comput Sci 2002 ; 42 : 305-316.
27. Willett P. Searching techniques for databases of two - and three - dimensional chemical structures. J Med Chem 2005 ; 48 : 4183-4199.
28. Gardiner E. Gillet V. Willett P. Cosgrove D. Representing clusters using a maximum common edge substructure algorithm applied to reduced graphs and molecular graphs. J Chem Inf Model 2007 ; 47 : 354-366.
29. Hessler G. Zimmermann M. Matter H. Evers A. Naumann T. Lengauer T. Rarey M. Multiple - ligand - based virtual screening: Methods and applications of the MTree approach. J Med Chem 2005 ; 48 : 6575-6584.
30. Barker E. Buttar D. Cosgrove D. Gardiner E. Kitts P. Willett P. Gillet V. Scaffold hopping using clique detection applied to reduced graphs. J Chem Inf Model 2006 ; 46 : 503-511.
31. McGregor M. Pallai P. Clustering of large databases of compounds: Using the MDL " keys " as structural descriptors. J Chem Inf Comput Sci 1997 ; 37 : 443-448.
32. Durant J. Leland B. Henry D. Nourse J. Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci 2002 ; 42 : 1273-1280.
33. Mason J. Morize I. Menard P. Cheney D. Hulme C. Labaudiniere R. New 4 - point pharmacophore method for molecular similarity and diversity applications: Overview of the method and applications, including a novel approach to the design of combinatorial libraries containing privileged substructures. J Med Chem 1999 ; 42 : 3251-3264.
34. Carhart RE. Smith DH. Venkataraghavan R. Atom pairs as molecular features in structure - activity studies: Defi nition and applications. J Chem Inf Comput Sci 1985 ; 25 : 64-73.
35. Daylight Theory Manual. Aliso Viejo, CA : Daylight Chemical Information Systems, Inc. 2002. Available at http://www.daylight.com/dayhtml/doc/theory/ (accessed February 1, 2008).
36. Bender A. Mussa HY. Glen RC. Reiling S. Molecular similarity searching using atom environments, information - based feature selection, and a na ï ve Bayesian classifi er. J Chem Inf Comput Sci 2004 ; 44 : 170-178.
37. Klon A. Glick M. Thoma M. Acklin P. Davies J. Finding more needles in the haystack: A simple and effi cient method for improving high - throughput docking results. J Med Chem 2004 ; 47 : 2743-2749.
38. Klon A. Glick M. Davies J. Application of machine learning to improve the results of high - throughput docking against the HIV - 1 protease. J Chem Inf Comput Sci 2004 ; 44 : 2216-2224.
39. Bender A. Mussa HY. Glen RC. Reiling S. Similarity searching of chemical databases using atom environment descriptors (MOLPRINT 2D): Evaluation of performance. J Chem Inf Comput Sci 2004 ; 44 : 1708-1718
40. Bender A. Mussa H. Gill G. Glen R. Molecular surface point environments for virtual screening and the elucidation of binding patterns (MOLPRINT 3D). J Med Chem 2004 ; 47 : 6569-6583.
41. Xue L. Godden J. Stahura F. Bajorath J. Design and evaluation of a molecular fi ngerprint involving the transformation of property descriptor values into a binary classifi cation scheme. J Chem Inf Comput Sci 2003 ; 43 : 1151-1157.
42. Eckert H. Bajorath J. Design and evaluation of a novel class - directed 2D fi ngerprint to search for structurally diverse active compounds. J Chem Inf Model 2006 ; 46 : 2515-2526.
43. Bajorath J. Selected concepts and investigations in compound classifi cation, molecular descriptor analysis, and virtual screening. J Chem Inf Comput Sci 2006 ; 41 : 233-245.
44. Engels MFM. Venkatarangan P. Smart screening: Approaches to effi cient HTS. Curr Opin Drug Discov Devel 2001 ; 4 : 275-283.
45. Downs GM. Barnard JM. Clustering methods and their uses in computational chemistry. In: Reviews in Computational Chemistry. Vol. 18. edited by Lipkowitz KB. Boyd DB. pp. 1-40. Weinheim : Wiley - WCH. 2002.
46. Ward JH. Hierarchical grouping to optimize an objective function. J Am Stat Assoc 1963 ; 58 : 236-244.
47. Duda RO. Hart PE. Stork DG. Pattern Classifi cation. 2nd edn. New York : Wiley Interscience. 2000.
48. Jarvis R. Patrick E. Clustering using a similarity measure based on shared near neighbors. IEEE Trans Comput 1973 ; C22 : 1025-1034.
49. Pearlman R. Smith K. Metric validation and the receptor - relevant subspace concept. J Chem Inf Comput Sci 1999 ; 39 : 28-35.
50. Godden J. Xue L. Kitchen D. Stahura F. Schermerhorn E. Bajorath J. Median partitioning: A novel method for the selection of representative subsets from large compound pools. J Chem Inf Comput Sci 2002 ; 42 : 885-893.
51. Chen X. Rusinko A. Young S. Recursive partitioning analysis of a large structure -activity data set using three - dimensional descriptors. J Chem Inf Comput Sci 1998 ; 38 : 1054-1062.
52. Rusinko A. Farmen M. Lambert C. Brown P. Young S. Analysis of a large structure/ biological activity data set using recursive partitioning. J Chem Inf Comput Sci 1999 ; 39 : 1017-1026.
53. Johnson M. Maggiora G. Concepts and Applications of Molecular Similarity. New York : John Wiley and Sons. 1990.
54. Willett P. Chemical similarity searching. J Chem Inf Comput Sci 1998 ; 38 : 983-996.
55. Molecular Drug Data Report (MDDR). San Leandro, CA : Elsevier MDL. Available at http://www.mdl.com (accessed February 1, 2008).
56. Olah M. Mracec M. Ostopovici L. Rad R. Bora A. Hadaruga N. Olah I. Banda M. Simon Z. Mracec M. Oprea TI. WOMBAT: World of molecular bioactivity. In: Chemoinformatics in Drug Discovery. edited by Oprea TI. pp. 223-239. New York : Wiley - VCH. 2004.
57. Salim N. Holliday J. Willett P. Combination of fi ngerprint - based similarity coeffi cients using data fusion. J Chem Inf Comput Sci 2003 ; 43 : 435-442.
58. Hert J. Willett P. Wilton DJ. Acklin P. Azzaoui K. Jacoby E. Schuffenhauer A. Comparison of fi ngerprint - based methods for virtual screening using multiple bioactive reference structures. J Chem Inf Comput Sci 2004 ; 44 : 1177-1185.
59. Whittle M. Gillet VJ. Willett P. Analysis of data fusion methods in virtual screening: Theoretical model. J Chem Inf Model 2006 ; 46 : 2193-2205.
60. Whittle M. Gillet VJ. Willett P. Analysis of data fusion methods in virtual screening: Similarity and group fusion. J Chem Inf Model 2006 ; 46 : 2206-2219.
61. Xue L. Stahura F. Godden J. Bajorath J. Fingerprint scaling increases the probability of identifying molecules with similar activity in virtual screening calculations. J Chem Inf Comput Sci 2001 ; 41 : 746-753.
62. Godden J. Furr J. Xue L. Stahura F. Bajorath J. Molecular similarity analysis and virtual screening by mapping of consensus positions in binary - transformed chemical descriptor spaces with variable dimensionality. J Chem Inf Comput Sci 2004 ; 44 : 21-29.
63. Eckert H. Bajorath J. Determination and mapping of activity - specific descriptor value ranges for the identifi cation of active compounds. J Med Chem 2006 ; 49 : 2284-2293.
64. Eckert H. Vogt I. Bajorath J. Mapping algorithms for molecular similarity analysis and ligand - based virtual screening: Design of DynaMAD and comparison with MAD and DMC. J Chem Inf Model 2006 ; 46 : 1623-1634.
65. Xia X. Maliski E. Gallant P. Rogers D. Classifi cation of kinase inhibitors using a Bayesian model. J Med Chem 2004 ; 47 : 4463-4470.
66. Vogt M. Godden J. Bajorath J. Bayesian interpretation of a distance function for navigating high - dimensional descriptor spaces. J Chem Inf Model 2007 ; 47 : 39-46.
67. Vogt M. Bajorath J. Bayesian screening for active compounds in high - dimensional chemical spaces combining property descriptors and molecular fi ngerprints. Chem Biol Drug Des 2008 ; 71 : 8-14.
68. Watson P. Naïve Bayes classifi cation using 2D pharmacophore feature triplet vectors. J Chem Inf Model 2008 ; 48 : 166-178.
69. Sun H. A naive Bayes classifi er for prediction of multidrug resistance reversal activity on the basis of atom typing. J Med Chem 2005 ; 48 : 4031-4039.
70. Nidhi. Glick M. Davies JW. Jenkins JL. Prediction of biological targets for compounds using multiple - category Bayesian models trained on chemogenomics databases. J Chem Inf Model 2006 ; 46 : 1124-1133.
71. Klon AE. Lowrie JF. Diller DJ. Improved na ï ve Bayesian modeling of numerical data for absorption, distribution, metabolism and excretion (ADME) property prediction. J Chem Inf Model 2006 ; 46 : 1945-1956.
72. Vogt M. Bajorath J. Bayesian similarity searching in high - dimensional descriptor spaces combined with Kullback - Leibler descriptor divergence analysis. J Chem Inf Model 2008 ; 48 : 247-255.
73. Labute P. Binary QSAR: A new method for the determination of quantitative structure activity relationships. In: Pacifi c Symposium on Biocomputing. Vol. 4. edited by Altman RB. Dunber AK. Hunter L. Klein TE. pp. 444-455. Singapore : World Scientifi c Publishing. 1999.
74. Godden J. Bajorath. J. A distance function for retrieval of active molecules from complex chemical space representations. J Chem Inf Model 2006 ; 46 : 1094-1097.
75. Ormerod A. Willett P. Bawden D. Comparison of fragment weighting schemes for substructural analysis. Quant Struct Act Relat 1989 ; 8 : 115-129.
76. Wilton DJ. Harrison RF. Willett P. Delaney J. Lawson K. Mullier G. Virtual screening using binary kernel discrimination: Analysis of pesticide data. J Chem Inf Model 2006 ; 46 : 471-477.
77. Cramer R. Redl G. Berkoff C. Substructural analysis. A novel approach to the problem of drug design. J Med Chem 1974 ; 17 : 533-535.
78. Kullback S. Information Theory and Statistics. Mineola, MN : Dover Publications. 1997.
79. Vogt M. Bajorath J. Introduction of an information - theoretic method to predict recovery rates of active compounds for Bayesian in silico screening: Theory and screening trials. J Chem Inf Model 2007 ; 47 : 337-341.
80. Vogt M. Bajorath J. Introduction of a generally appicable method to estimate retrieval of active molecules for similarity searching using fi ngerprints. ChemMedChem 2007 ; 2 : 1311-1320.
81. Harper G. Bradshaw J. Gittins JC. Green DVS. Leach AR. Prediction of biological activity for high - throughput screening using binary kernel discrimination. J Chem Inf Comput Sci 2001 ; 41 : 1295-1300.
82. Wilton D. Willett P. Lawson K. Mullier G. Comparison of ranking methods for virtual screening in lead - discovery programs. J Chem Inf Comput Sci 2003 ; 43 : 469-474.
83. Chen B. Harrison RF. Pasupa K. Willett P. Wilton DJ. Wood DJ. Lewell XQ. Virtual screening using binary kernel discrimination: Effect of noisy training data and the optimization of performance. J Chem Inf Model 2006 ; 46 : 478-486.
84. Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 1998 ; 2 : 121-167.
85. Vapnik VN. The Nature of Statistical Learning Theory. 2nd edn. New York : Springer. 2000.
86. Stahura FL. Bajorath J. Virtual screening methods that complement HTS. Comb Chem High Throughput Screen 2004 ; 7 : 259-269.
87. Blower PE. Cross KP. Eichler GS. Myatt GJ. Weinstein JN. Yang C. Comparison of methods for sequential screening of large compound sets. Comb Chem High Throughput Screen 2006 ; 9 : 115-122.
88. Parker CN. Bajorath J. Towards unifi ed compound screening strategies: A critical evaluation of error sources in experimental and virtual high - throughput screening. QSAR Comb Sci 2006 ; 25 : 1153-1161.
89. Jones-Hertzog DK. Mukhopadhyay P. Keefer CE. Young SS. Use of recursive partitioning in the sequential screening of G - protein - coupled receptors. J Pharmacol Toxicol Methods 1999 ; 42 : 207-215.
90. Abt M. Lim Y. Sacks J. Xie M. Young SS. A sequential approach for identifying lead compounds in large chemical databases. Stat Sci 2001 ; 16 : 154-168.
91. van Rhee AM. Use of recursion forests in the sequential screening process: Consensus selection by multiple recursion trees. J Chem Inf Comput Sci 2003 ; 43 : 941-948.
92. Auer J. Bajorath J. Simulation of sequential screening experiments using emerging chemical patterns. Med Chem 2008 ; 4 : 80-90.
93. Deng Z. Chuaqui C. Singh J. Knowledge - based design of target - focused libraries using protein - ligand interaction constraints. J Med Chem 2006 ; 49 : 490-500.
94. Orry AJ. Abagyan RA. Cavasotto CN. Structure - based development of target -specifi c compound libraries. Drug Discov Today 2006 ; 11 : 261-266.