Comparative study of machine-learning and chemometric tools for analysis of in-vivo high-throughput screening data

Journal of Chemical Information and Modeling

Volumn 48, Issue 8, 2008, Pages 1663-1668

  Simmons, Kirk ^a   Kinney, John ^b   Owens, Aaron ^b   Kleier, Dan ^c   Bloch, Karen ^b   Argentar, Dave ^d   Walsh, Alicia ^b   Vaidyanathan, Ganesh ^e  

^a Simmons Consulting   (United States)

^b DUPONT COMPANY   (United States)

^c DREXEL UNIVERSITY   (United States)

^d Small Bear Technical Consulting   (United States)

^e Quantum Leap Innovations Inc   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOACTIVITY; CLASSIFICATION (OF INFORMATION); COST EFFECTIVENESS; CROPS; MACHINE LEARNING;

BIOLOGICALLY ACTIVE COMPOUNDS; CHEMOMETRIC TOOLS; COMPARATIVE STUDIES; COST EFFECTIVE; CROP PROTECTION; FALSE NEGATIVES; HIGH-THROUGHPUT SCREENING; PREDICTIVE PERFORMANCE;

DATA STREAMS;

ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; COMBINATORIAL CHEMISTRY; COMPARATIVE STUDY; DRUG SCREENING;

ARTIFICIAL INTELLIGENCE; COMBINATORIAL CHEMISTRY TECHNIQUES; DRUG EVALUATION, PRECLINICAL; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER);

EID: 52049117427     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci800142d     Document Type: Article

References (32)

1. Luco, J. M. Prediction of the Brain-Blood Distribution of a Large Set of Drugs from Structurally Derived Descriptors Using Partial Least-Squares (PLS) Modeling. J. Chem. Inf. Comput. Sci. 1999, 39 (2), 396-404.
2. Basak, S. C.; Grunwald, G. D.; Gute, B. D.; Balasubramanian, K.; Opitz, D. Use of Statistical and Neural Net Approaches in Predicting Toxicity of Chemicals. J. Chem. inf. Comput. Sci. 2000, 40 (4), 885-890.
3. English, N. J.; Carroll, D. G. Prediction of Henry's Law Constants by a Quantitative Structure Property Relationship and Neural Networks. J. Chem. Inf. Comput. Sci. 2001, 41 (5), 1150-1161.
4. Mosier, P. D.; Jurs, P. C. QSAR/QSPR Studies Using Probabilistic Neural Networks and Generalized Regression Neural Networks. J. Chem. Inf. Comput. Sci. 2002, 42 (6), 1460-1470.
5. Lind, P.; Maltseva, T. Support Vector Machines for the Estimation of Aqueous Solubility. J. Chem. Inf. Comput. Sci. 2003, 43 (6), 1855-1859.
6. Sutherland, J. J.; O'Brien, L. A.; Weaver, D. F. Spline-Fitting with a Genetic Algorithm: A Method for Developing Classification Structure-Activity Relationships. J. Chem. Inf. Comput. Sci. 2003, 43 (6), 1906-1915.
7. Beger, R. D.; Young, J. F.; Fang, H. Discriminant Function Analyses of Liver-Specific Carcinogens. J. Chem. Inf. Comput. Sci. 2004, 44 (3), 1107-1110.
8. Zheng, W.; Tropsha, A. Novel Variable Selection Quantitative Structure-Property Relationship Approach Based on the k-Nearest-Neighbor Principle. J. Chem. Inf. Comput. Sci. 2000, 40 (1), 185-194.
9. Simmons, K. Empirical Validation of the Effectiveness of Chemical Descriptors in Data Mining, 2nd Joint Sheffield Conference on Cheminformatics: Computational Tools for Lead Discovery, University of Sheffield, Sheffield, U.K., April 9, 2001.
10. Bruce, C. L.; Melville, J. L.; Pickett, S. D.; Hirst, J. D. Contemporary QSAR Classifiers Compared. J. Chem. Inf. Model. 2007, 47(1), 219-227.
11. Plewczynski, D.; Spieser, S. A. H.; Koch, U. Assessing Different Classification Methods for Virtual Screening. J. Chem. Inf. Model. 2006, 46(3), 1098-1106.
12. Bender, A.; Glen, R. C. A Discussion of Measures of Enrichment in Virtual Screening: Comparing the Information Content of Descriptors with Increasing Levels of Sophistication. J. Chem. Inf. Model. 2005, 45(5), 1369-1375.
13. Yao, X. J.; Panaye, A.; Doucet, J. P.; Zhang, R. S.; Chen, H. F.; Liu, M. C.; Hu, Z. D.; Fan, B. T. Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression. J. Chem. Inf. Comput. Sci. 2004, 44 (A), 1257-1266.
14. Feng, J.; Lurati, L.; Ouyang, H.; Robinson, T.; Wang, Y.; Yuan, S.; Young, S. Predictive Toxicology: Benchmarking Molecular Descriptors and Statistical Methods. J. Chem. Inf. Comput. Sci. 2003, 43 (5), 1463-1470.
15. Simmons, K. Practical Outcomes of Data Mining in-Vivo HTS Data, Proceedings of the Conference on Exploiting Molecular Diversity, San Diego, CA, Cambridge Health Tech, 2002.
16. Pipeline Pilot, version 4, Scitegic. http://www.scitegic.com (accessed July 19, 2008).
17. MDL connection table specifications available at MDL, Inc. http:// www.mdli.com (accessed July 19, 2008).
18. Concord is available from Tripos, Inc. http://www.tripos.com/data/ SYBYL/Concord_072505.pdf (accessed July 19, 2008). For the specifications of the mol2 format, see: http://tripos.com/tripos_ resources/fileroot/ mol2_format_Dec07.pdf (accessed July 19, 2008).
19. Todeschini, R.; Consonni, V. Handbook of Molecular Descriptors; Mannhold, R., Kubinyi, H., Timmerman, H., Eds.; WILEY-VCH Verlag Gmbh:Weinheim, Germany, 2000.
20. Carhart, R.; Smith, D. H.; Venkataraghavan, R. J. Atom pairs as molecular features in structure-activity studies: definition and applications. J. Chem. inf. Comput. Sci. 1985, 25, 64-73.
21. Many thanks to Prof. Alex Tropsha, University of North Carolina.
22. BASE SAS, Version 6; SAS Institute Inc.: Cary, NC, http:// www.sas.com (accessed July 19, 2008).
23. PLS Toolbox for MatLab; Eigenvector Research, Inc.: 3905 West Eaglerock Drive, Wenatchee, WA 98801. http://www.eigenvector.com (accessed July 19, 2008).
24. Owens, A. J.; Filkin, D. L. Efficient training of the Back Propagation Network by solving a system of stiff ordinary differential equations. International Joint Conference on Neural Networks, II, Washington, DC, 1989; pp 381-386.
25. Vaidyanathan, G. InfoEvolve - Moving from Data to Knowledge Using Information Theory and Genetic Algorithms. Ann. N.Y. Acad. Sci. 2004, 1020, 227-238.
26. Hawkins, D. Formal Inference-Based Recursive Modeling, version 2.3; Univ. of Minnesota: Duluth, MN, 1999.
27. Murphy, S. K.; Kasif, K.; Salzberg, S. A System for Induction of Oblique Decision Trees. J. Artificial Intelligence Res. 1994, 2, 1-32.
28. Quinlan, J. R. C4.5: Programs for Machine Learning; Morgan Kaufmann:San Mateo, CA, 1993.
29. CART Pro 6; Salford Systems: San Diego, CA. 2006.http://www. salford-systems.com (accessed July 19, 2008).
30. Brown, R.; Martin, Y. 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.
31. Brown, R.; Martin, Y. The Information Content of 2D and 3D Structural Descriptors Relevant to Ligand-Receptor Binding. J. Chem. Inf. Comput. Sci. 1997, 37, 1-9.
32. MolconnZ version 3.50; EduSoft: Ashland, VA. http://www.edusoft-lc. com/molconn (accessed July 19, 2008).