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Inference Networks for Molecular Database Similarity Searching
Ammar Abdo, Naomie Salim
Pages - 1 - 16     |    Revised - 15-02-2008     |    Published - 30-02-2008
Volume - 2   Issue - 1    |    Publication Date - February 2008  Table of Contents
Bayesian Networks, Molecular Similarity Searching, Chemical Databases, Inference Network, Drug Discovery.
Molecular similarity searching is a process to find chemical compounds that are similar to a target compound. The concept of molecular similarity play an important role in modern computer aided drug design methods, and has been successfully applied in the optimization of lead series. It is used for chemical database searching and design of combinatorial libraries. In this paper, we explore the possibility and effectiveness of using Inference Bayesian network for similarity searching. The topology of the network represents the dependence relationships between molecular descriptors and molecules as well as the quantitative knowledge of probabilities encoding the strength of these relationships, mined from our compound collection. The retrieve of an active compound to a given target structure is obtained by means of an inference process through a network of dependences. The new approach is tested by its ability to retrieve seven sets of active molecules seeded in the MDDR. Our empirical results suggest that similarity method based on Bayesian networks provide a promising and encouraging alternative to existing similarity searching methods.
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2 M. R. Dikhit, G.C.Sahoo and P.Das, “JEVBase: An Interactive Resource for Protein Annotationof JE Virus” International Journal of Biometrics and Bioinformatics (IJBB), 4(3), pp. 31-66, Aug. 2009.
1 Google Scholar 
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5 CiteSeerX 
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13 WorldCat 
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15 PdfSR 
1 M. A. Johnson and G. M. Maggiora. “Concepts and Application of Molecular Similarity”, John Wiley & Sons, New York (1990)
2 R. P. Sheridan and S. K. Kearsley. “Why do we need so many chemical similarity search methods?”. Drug Discov. Today, 7, 903–911, 2002
3 M. A. Miller. “Chemical Database Techniques in Drug Discovery”. Nature Reviews Drug Discov.,1, pp. 220-227, 2002
4 P. Willett. “Chemoinformatics: an application domain for information retrieval techniques”. In Proceedings of the 27th Annual international ACM SIGIR Conference on Research and Development in information Retrieval SIGIR '04. ACM, New York, NY, 393-393, 2004
5 P. Willett, J. M. Barnard and G. M. Downs. “Chemical similarity searching”. Journal of Chemical Information and Computer Sciences, 38:983-996, 1998
6 P. M. Dean. “Molecular Similarity In Drug Design”. Blackie Academic & Professional, London, 1995
7 R. E. Carhart, D. H. Smith and R. Venkataraghavan. “Atom pairs as molecular features in structure-activity studies: definitions and applications”. Journal of Chemical Information and Computer Science, 25:64-73, 1985
8 D. E. Patterson, R. D. Cramer, A. M. Ferguson, R. D. Clark and L. E. Weinberger. “Neighborhood behavior: as useful concept for validation of molecular diversity descriptors”. Journal of Medical Chemistry, 39:3060-3069, 1996
9 P. Willett, V. Winterman and D. Bawden. “Implementation of nearest neighbour searching in an online chemical structure search system”. Journal of Chemical Information and Computer Science, 26:36-41, 1986
10 T. R. Hagadone. “Molecular substructure similarity searching: efficient retrieval in twodimensional structure databases”. Journal of Chemical Information and Computer Science. 32:515-521, 1992
11 W. Fisanick, K. P. Cross and A. Rusinko. “Similarity searching on CAS Registry Substances. 1. Global molecular property and generic atom triangle geometric searching”. Journal of Chemical Information and Computer Sciences, 32:664-674, 1992
12 D. Bawden. “Molecular dissimilarity in chemical information systems”. In Chemical Structures Vol. 2: The International Language of Chemistry (W. A. Warr, ed.), Springer-Verlag, Hiedelberg, pp. 383-388, 1993
13 M. S. Lajiness. “Dissimilarity-based compound selection techniques”. Perspectives in Drug Discovery and Design, 7/8:65-84, 1997
14 E. J. Martin, J. M. Blaney, M. A. Siani, D. C. Spellmeyer, A. K. Wong and W. H. Moos. “Measuring diversity: Experimental design of combinatorial libraries for drug discovery. Journal of Medicinal Chemistry, 38:1431-1436, 1995
15 J. D. Holliday and P. Willett. “Definitions of "dissimilarity" for dissimilarity-based compound selection”. Journal of Biomolecular Screening, 1:145-151, 1996
16 V. J. Gillet, P. Willett and J. Bradshaw. “The effectiveness of reactant pools for generating structurally diverse combinatorial libraries”. Journal of Chemical Information and Computer Science. 37:731-740, 1997
17 P. Willett. “Similarity-based virtual screening using 2D fingerprints”. Drug Discov. Today, 1046-1053, 2006
18 P. G. Dittmar, N. A. Farmer, W. Fisanick, R. C. Haines and J. Mockus. “The CAS online search system. 1. General system design and selection, generation and use of search screens”. Journal of Chemical Information and Computer Sciences, 23:93-102, 1983
19 Barnard Chemical Information Ltd., “Barnard Chemical Information Fingerprint Software Documentation”. MAKEBITS version 3.3, p. 1-5, 1997
20 Barnard Chemical Information Ltd., “Barnard Chemical Information Fingerprint Software Documentation”. MAKEFRAG version 3.3, Sheffield, p. 1, 1997
21 J. L. Durant, B. A. Leland, D. R. Henry and J. G. Nourse. “MDL keys revisited”. 2nd Joint Sheffield Conference on Chemoinformatics: Computational Tools For Lead Discovery, University of Sheffield, Sheffield, 2001
22 J. L. Durant, B. A. Leland, D. R. Henry and J. G. Nourse. “Reoptimization of MDL keys for use in drug discovery”. Journal of Chemical Information and Computer Science, 42:1273- 1280, 2002
23 Tripos Inc. UNITY Reference Guide version 4.1. Tripos, St. Louis, Missouri, 1999
24 C. A James, D. Weininger and J. Delany. “Daylight Theory Manual” http://www.daylight.com/dayhtml/doc/theory/index.html
25 G. M. Downs and P. Willett. “Similarity searching in databases of chemical structures”. In: K. B. Lipkowitz and D. B. Boyd (Eds.), Reviews in Computational Chemistry, VCH Publishers, New York, Vol. 7, pp. 1-66, 1996
26 L. Hodes. “Clustering a large number of compounds. 1. Establishing the method on an initial sample”. Journal of Chemical Information and Computer Science, 29:66-71, 1989
27 P. Willett and V. Winterman. “A comparison of some measures of intermolecular structural similarity”. Quantitative Structure-Activity Relationships, 5, 18–25, 1986
28 P. Willett. “Algorithms for calculation of similarity in chemical structure databases”. In Concepts and Application of Molecular Similarity, M. A. Johnson and G. M. Maggiora, Eds., John Wiley and Sons, New York. pp. 43-61, 1990
29 P. H. A. Sneath and R. R. Sokal. “Numerical Taxanomy”. Freeman, San Francisco, 1973
30 P. Willett. “Similarity And Clustering In Chemical Information Systems”, Research Studies Press, Letchworth, (1987)
31 D. Ellis, J. Furner-Hines and P. Willett. “Measuring the degree of similarity between objects in text retrieval systems”. Perspective in Information Management. 3:128-149, 1993
32 G. W. Adamson and J. A. Bush. “A method for the automatic classification of chemical structures”. Information Storage and Retrieval, 9:561-568,1973
33 J. Pearl. “Probabilistic reasoning in intelligent systems: Networks of plausible inference”, Morgan Kaufmann Publishers, (1988)
34 G. Salton and M. J. McGill. “Introduction to Modern Information Retrieval”, McGraw-Hill, NewYork, (1983)
35 C. J. Van Rijsbergen. “Information Retrieval”, 2nd ed., University of Glasgow, 87-110 (1979)
36 H. Turtle. “Inference Networks for Document Retrieval”. PhD Thesis, University of Massachusetts, 1990
37 H. Turtle and W. Croft. “A comparison of text retrieval models”. Comput. Journal, 35, 279- 290, 1992
38 B. A. N. Ribeiro and R. Muntz. “A belief network model for IR”. In: Proceedings of the 19th ACM SIGIR Conference, pp. 253–260,1996
39 S. K. M. Wong and Y. Y Yao. “On modeling information retrieval with probabilistic inference”. ACM Transactions on Information Systems, Vol. 13, No. 1, pp. 38-68, 1995
40 H. Turtle and W. Croft. “Evaluation of an inference network-based retrieval model”. ACM Transactions on Information Systems, 9:187-222, 1991
41 Barnard Chemical Information Ltd., “Barnard Chemical Information Fingerprint”. http://www.bci.gb.com
42 J. Gasteiger and T. Engel. ”Chemoinformatics”, VCH-Wiley, New York, Vol. 1, pp. 3-5 (2003)
43 L. M. De Campos, J. M. Fernández and J. F. Huete. “The BNR model: foundations and performance of a Bayesian network- based retrieval model”. Int. J. Approx. Reasoning, 3, pp. 265–285, 2003
44 Molecular Design Ltd., MDDR “MDL Drug Data Report Database”. http://www.mdli.com
45 Melano Chemoinformatics. “Dragon software”. http://www.talete.mi.it
46 N. Salim, J. Holliday and P. Willet. “Combination of fingerprint-based similarity coefficients using data fusion”. J. Chem. Inf. Comput. Sci., 43, pp. 435-442, 2003
47 P.A. Bath, C. A. Morris and P. Willett. “Effect of standardisation of fragment-based measures of structural similarity”. Journal of Chemometrics, 7, pp. 543, 1993.
48 N. Daut, R. Mohemad and N. Salim. “Finding Best Coefficients for Similarity Searching Using Neural Network Algorithm”. International Conference in Artificial Intelligence in Engineering & Technology (ICAIET), 2006.
49 Downs, G.M., Poirrette, A.R., Walsh, P. and Willett, P. “Evaluation of similarity searching methods using activity and toxicity data”. In Chemical Structures Vol. 2: The International Language of Chemistry (W. A. Warr, ed), Springer Verlag, Heidelberg, pp. 409-421, 1993
50 N. Salim and W. W. P. Godfrey. “Effectiveness of Probability Models for Compound Similarity Searching”. Journal of Advancing Information Management Studies, 2(1): pp. 56-74, 2005.
Mr. Ammar Abdo
- Malaysia
Mr. Naomie Salim
- Malaysia