Data processing: artificial intelligence – Knowledge processing system – Knowledge representation and reasoning technique
Reexamination Certificate
2005-06-07
2005-06-07
Starks, Jr., Wilbert L. (Department: 2121)
Data processing: artificial intelligence
Knowledge processing system
Knowledge representation and reasoning technique
C702S022000
Reexamination Certificate
active
06904423
ABSTRACT:
A system for analyzing a vast amount of data representative of chemical structure and activity information and concisely providing conclusions about structure-to-activity relationships. A computer may adaptively learn new substructure descriptors based on its analysis of the input data. The computer may then apply each substructure descriptor as a filter to establish new groups of molecules that match the descriptor. From each new group of molecules, the computer may in turn generate one or more additional new groups of molecules. A result of the analysis in an exemplary arrangement is a tree structure that reflects pharmacophoric information and efficiently establishes through lineage what effect on activity various chemical substructures are likely to have. The tree structure can then be applied as a multi-domain classifier, to help a chemist classify test compounds into structural subclasses.
REFERENCES:
patent: 5025388 (1991-06-01), Cramer, III et al.
patent: 5263120 (1993-11-01), Bickel
patent: 5307287 (1994-04-01), Cramer, III et al.
patent: 5590218 (1996-12-01), Ornstein
patent: 5619709 (1997-04-01), Caid et al.
patent: 5684711 (1997-11-01), Agrafiotis et al.
patent: 5751605 (1998-05-01), Hurst et al.
patent: 5825909 (1998-10-01), Jang
patent: 6182016 (2001-01-01), Liang et al.
patent: 6294136 (2001-09-01), Schwartz
patent: 6625585 (2003-09-01), MacCuish et al.
patent: WO 98/47087 (1998-10-01), None
Hibert et al. “Graphics Computer-Aided Receptor Mapping as a Predicitve Tool for Drug Design: Development of Potent, Selective, and Stereospecific Ligans for the 5-HT1A Receptor.” J. Med. Chem., 1988, vol 31, pp. 1087-1093, Supplementary Mat. pp. 32-34.
van Osdol, W. W. et al., “Use of the Kohonen Self-organizing Map to Study the Mechanisms of Action of Chemotherpeutic Agents”,Journal of the National Cancer Institute, 86:1853-1859 (1994).
Ornstein, L., “Computer Learning and the Scientific Method: A Proposed Solution to the Information Theoretical Problem of Meaning”,Journal of the Mount Sinai Hospital, XXXII:437-494 (1965).
Barnard, J. M. and Downs, G. M., “Clustering of Chemical Structures on the Basis of Two-Dimensional Similarity Measures”,Journal of Chemical Information and Computer Sciences, 32:644-649 (1992).
Grethe, G. and Hounshell, W. D., “Similarity Searching in the Development of New Bioactive Compounts. An Application.”Chemical Structure Proceedings International Conference, pp. 399-407 (1993).
King, R. D. et al., “Comparison of Artificial Intelligence Methods for Modeling Pharmaceutical Qsars”,Applied Artificial Intelligence, 9:213-233 (1995).
Jain, K. J. et al., “Algorithms for Clustering Data”,Algorithms for Clustering Data, pp. 96-101 (1988).
Downs, G. M. and Willett, P., Similarity Searching and Clustering of Chemical-Structure Databases Using Molecular Property Data,J. Chem. Inf. Comput. Sci. 34: 1094-1102 (1994).
Kearsley, S. K. et al., Chemical Similarity Using Physiochemical Property Descriptors,J. Chem. Inf. Comput. Sci. 36: 118-127 (1996).
Brown, R. D. et al., Matching Two-Dimensional Chemical Graphs Using Genetic Algorithms,J. Chem. Inf. Comput. Sci. 34: 63-70 (1994).
Brown, R. D. and Martin, Y.C., Use of Structures—Activity of Data to Compare Structure-Based Clustering Methods and Descriptors for Use in Compound Selection,J. Chem. Inf. Comput. Sci. 36:572-584 (1996).
Discriminant Analysis and Clustering—Panel on Discriminant Analysis, Classification and Clustering.Statistical Science4: 34-69 (1989).
Regalado, A., Preclinical Strategies—Drug Development's Preclinical Bottleneck,Start-Uppp. 26-37 (Dec. 1997).
Longman, R., Marketplace Strategies—Screening the Screeners,Start-Uppp. 14-22 (Sep. 1997).
Thayer, A. M., Combinatorial chemistry becoming core technology at drug discovery companies,C&ENpp. 57-64 (Feb. 1996).
Combinatorial Chemistry—Combinatorial chemists focus on small molecules, molecular recognition, and automation,C&ENpp. 28-54 (Feb. 1996).
Kohonen, Self-Organizing Maps, Springer pp. 85-144. (1995).
Chen, X. et al., Recursive Partitioning Analysis of a Large Structure-Activity Data Set Using Three-Dimensional Descriptors1,J. Chem. Inf. Comput. Sci. (1998).
James, C. A. et al., Daylight Theory Manual Daylight 4.61, Daylight Chemical Information Systems, Inc., Version 11 Feb. , 1997.
Weininger, D., SMILES, a Chemical Language and Information System. 1. Introduction to Methodology and Encoding Rules.J. Chem. Inf. Comput. Sci. 28: 31-36 (1988).
Cook, D.J. et al., Knowledge Discovery from Stuctural Data.Journal of Intelligence and Information Sciences, vol. 5, No, 3, pp. 229-245 (1995).
Djoko, S. et al., An Empirical Study of Domain Knowledge and its Benefits to Substructure Discovery. InIEEE Transactions on Knowledge and Data Engineering, vol. 9, No. 4, pp. 1-13 (1997).
Galal, G. et al., Improving Scalability in a Knowledge Discovery System by Exploiting Parallelism. In the Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pp. 171-174 (1997).
Holder, L. B. and D. J. Cook. Discovery of Inexact Concepts from Structural Data. InIEEE Transactions of Knowledge and Data Engineering, vol. 5,No. 6, pp. 992-994 (1993).
Holder , L. B. et al., Fuzzy Substructure Discovery. InProceedings of the Ninth International Conference on Machine Learning, pp. 218-223 (1992).
Cook, D. J. et al., Scalable Discovery of Informative Structural Concepts Using Domain Knowledge. InIEEE Expert, vol. 11, No. 5, pp. 59-68 (1996).
Aude, J.C. et al., Applications of the pyramidal clustering method to biological objects. Computers & Chemistry 23: 301-315 (1999).
Bertrand, P., Structural Properties of Pyramidal Clustering. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 19: 35-53 (1995).
Fausett, L., Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. pp. 169-188 (1994).
Godden, Jeffrey W., et al. Combinatorial Preferences Affect Molecular Similarity/Diversity Calculations Using Binary Fingerprints and Tanimoto Coefficients.J. Chem. Inf. Comput. Sci. 2000, vol. 40, pp. 163-166.
Downs, G.M. and Willett, P., “Similarity Searching and Clustering of Chemical-Structure Databases Using Molecular Property Data”,J. Chem. Inf. Comput. Sci. 34:1094-1102 (1994).
Kearsley, S.K. et al., Chemical Similarity Using Physiochemical Property Descriptors,J. Chem. Inf. Comput. Sci, 36:118-127 (1996).
Brown, R.D., et al., Matching Two-Dimensional Chemical Graphs Using Genetic Algorithms,J. Chem. Inf. Comput. Sci. 34:63-70 (1994).
Brown, R.D. and Martin, Y.C., Use of Structure-Activity Data to Compare Structure-Based Clustering Methods and Descriptors for Use in Compound Selection,J. Chem. Inf. Comput. Sci. 36:572-584 (1996).
Barnard, J.M. and Downs, G.M., Chemical Fragment Generation and Clusteriing Software, Product Descriptions, Jun. 27, 1996.
Kohonen, T., Self-Organizing Maps, Springer, pp. 85-144.
Longman, R., Marketplace Strategies—Screening the Screeners.Start-Up, pp. 14-22 (Sep. 1997).
Thayer, A.M., Combinatorial Chemistry becoming core technology at drug discovery companies.C&EN, pp. 57-67 (Feb. 1996).
Goodacre, R. et al., Quantitative Alanysis of Multivariate Data using Artificial Neural Networks: A Tutorial Review and Applications to the Deconvolution of Pyrolysis Mass Spectra,Tutorial from >I≦ Zentralblatr fur Bakteriologie(1999).
James, C.A. et al., Daylight Theory Manual Daylight 4.61, Daylight Chemical Information Systems, Inc., Version 11 (Feb. 1997).
Labute, P., Binary QSAR: A new Technology for HTS and UHTS Data Analysis, Chemical Computing Group, Inc. InJournal of the Chemical Computing Group(1998).
From the World Wide Web: www.netsci.org, Network Science—Welcome to NetSci's Lists of Computational Chemistry Software (1999), printed Feb. 8, 1999.
From the World Wide Web: www.netsci.org: Network Science—Welcome to NetSci's Combinatorial Chemistry and Mass Screening YellowPages (1999), printed Feb. 8, 1
Bassett Susan I.
Kelley Brian P.
Nicolaou Christodoulos A.
Nutt Ruth F.
Bioreason, Inc.
Starks, Jr. Wilbert L.
LandOfFree
Method and system for artificial intelligence directed lead... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Method and system for artificial intelligence directed lead..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method and system for artificial intelligence directed lead... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3466105