|
| Inference Networks for Molecular Database Similarity Searching
|
|
Full
text: |
 PDF(202.8KB) |
|
|
Source |
International Journal of Biometrics and Bioinformatics (IJBB) |
|
Table of Contents |
|
|
Download
Complete Issue PDF(416.47KB) |
|
Volume: 2 Issue: 1 |
| |
Pages: 1-16 |
|
Publication
Date: February 2008 |
|
ISSN
(Online): 1985-2347 |
|
|
|
|
|
Pages |
1 - 16 |
|
Author(s) |
|
|
|
Published
Date |
30-02-2008 |
|
Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
|
ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
| |
|
| |
|
|
|
| |
|
|
| This Manuscript is indexed in the following databases/websites:- |
|
| 1. Directory of Open Access Journals (DOAJ) |
| 2. Docstoc |
| 3. Scribd |
| 4. PDFCAST |
| 5. Google Scholar |
| 6. WorldCat |
| 7. CiteSeerX |
| 8. ScientificCommons |
| 9. Academic Index |
| 10. refSeek |
| 11. Bielefeld Academic Search Engine (BASE) |
| 12. ResearchGATE |
| 13. Socol@r |
| 14. iSEEK |
| 15. Academic Journals Database |
| 16. Libsearch |
| |
|
| |
|
|
| 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. |
| |
|
| |
|
| |
| 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.
|
|
|
| |
|
| |
|
| |
| 1 |
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 |
Academia
|
| 2 |
Faculty of Computer Science & Information Systems - Universiti Teknologi Malaysia (UTM)
|
| 3 |
Academia Research
|
| 4 |
Academia.edu
|
| 5 |
yasni
|
| 6 |
Universiti Teknologi Malaysia
|
| |
|
| |
|
| |
|
| Ammar Abdo : Colleagues
|
|
| Naomie Salim : Colleagues
|
|
