Local lazy regression: Making use of the neighborhood to improve QSAR predictions

Journal of Chemical Information and Modeling

Volumn 46, Issue 4, 2006, Pages 1836-1847

  Guha, Rajarshi ^a   Dutta, Debojyoti ^b   Jurs, Peter C ^a   Chen, Ting ^b  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; DATABASE SYSTEMS; ERROR ANALYSIS; MATHEMATICAL MODELS; REGRESSION ANALYSIS;

ANOMALOUS MOLECULES; LOCAL LAZY REGRESSION (LLR); QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP (QSAR) MODELS; ROOT-MEAN-SQUARE ERROR (RMSE0;

MOLECULAR STRUCTURE;

ARTICLE; CHEMICAL MODEL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REGRESSION ANALYSIS;

MODELS, CHEMICAL; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; REGRESSION ANALYSIS;

EID: 33746928751     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci060064e     Document Type: Article

References (32)

1. Sheridan, R.; Hunt, P.; Culberson, J. Molecular Transformations as a Way of Finding and Exploiting Consistent Local QSAR. J. Chem. Inf. Model. 2006, 46, 180-192.
2. Pan, D.; Iyer, M.; Liu, J.; Li, Y.; Hopfinger, A. Constructing Optimum Blood Brain Barrier QSAR Models Using a Combination of 4D-Molecular Similarity Measures and Cluster Analysis. J. Chem. Inf. Model. 2004, 44, 2083-2098.
3. Klekota, J.; Brauner, E.; Schreiber, S. Identifying Biologically Active Compound Classes Using Phenotypic Screening Data and Sampling Statistics. J. Chem. Inf. Model. 2005, 45, 1824-1836.
4. He, L.; Jurs, P. Assessing the Reliability of a QSAR Model's Predictions. J. Mol. Graphics Modell. 2005, 23, 503-523.
5. Barakat, N.; Jiang, J.; Liang, Y.; Yu, R. Piece-Wise Quasi-Linear Modeling in QSAR and Analytical Calibration Based on Linear Substructures Detected by Genetic Algorithm. Chemom. Intell. Lab. Syst. 2004, 72, 73-82.
6. Shen, Q.; Jiang, J.-H.; Jiao, C.-X.; Huan, S.-Y.; Shen, G.-L.; Yu, R.-Q. Optimized Partition of Minimum Spanning Tree for Piecewise Modeling by Particle Swarm Algorithm. QSAR Studies of Antagonism of Angiotensin II Antagonists. J. Chem. Inf. Model. 2004, 44, 2027-2031.
7. Cressie, N. Statistics for Spatial Data; Wiley-Interscience: New York, 1993.
8. Fang, K.-T.; Yin, H.; Liang, Y.-Z. New Approach by Kriging Models to Problems in QSAR. J. Chem. Inf. Model 2004, 44, 2106-2113.
9. Birattari, M.; Bontempi, G.; Bersini, H. Lazy Learning Meets the Recursive Least Squares Algorithm. In Advances in Neural Information Processing Systems 11; MIT Press: Cambridge, MA, 1999; pp 375-381.
10. Bontempi, G.; Birattari, M.; Bersini, H. Local Learning for Iterated Time-Series Prediction. In International Conference on Machine Learning; Publisher: Place of Publication, 1999.
11. Aha, D.; Kibler, D.; Albert, M. Instance-Based Learning Algorithms. Mach. Learn. 1991, 6, 37-66.
12. Cleveland, W.; Devlin, S.; Grosse, S. Regression by Local Fitting: Methods, Prospectives and Computational Algorithms. J. Econometrics 1988, 37, 87-114.
13. Atkeson, C.; Moore, A.; Schall, S. Locally Weighted Learning. Artif. Intell. Rev. 1995, 11, 11-73.
14. Bentley, J. Multidimensional Divide-and-Conquer. Commun. ACM 1980, 23, 214-229.
15. Bozkaya, T.; Ozsoyoglu, M. Indexing Large Metric Spaces for Similarity Search Queries. ACM Trans. Database Syst. 1999, 24, 361-404.
16. Dutta, D.; Guha, R.; Jurs, P.; Chen, T. Scalable Partitioning and Exploration of Chemical Spaces Using Geometric Hashing. J. Chem. Inf. Model. 2006, 46, 321-333.
17. Martin, Y. C.; Kofron, J. L.; Traphagen, L. M. Do Structurally Similar Molecules Have Similar Biological Activity? J. Med. Chem. 2002, 45, 4350-4358.
18. R Development Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2005; ISBN 3-900051-07-0.
19. Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning; Springer: New York, 2001.
20. Guha, R.; Jurs, P. The Development of QSAR Models To Predict and Interpret the Biological Activity of Artemisinin Analogues. J. Chem. Inf. Comput. Sci. 2004, 44, 1440-1449.
21. Avery, M. A.; Alvim-Gaston, M.; Rodrigues, C. R.; Barreiro, E. J.; Cohen, F. E.; Sabnis, Y. A.; Woolfrey, J. R. Structure Activity Relationships of the Antimalarial Agent Artemisinin. The Development of Predictive in Vitro Potency Models Using CoMFA and HQSAR Methodologies. J. Med. Chem. 2002, 45, 292-303.
22. Guha, R.; Jurs, P. Development of Linear, Ensemble, and Nonlinear Models for the Prediction and Interpretation of the Biological Activity of a Set of PDGFR Inhibitors. J. Chem. Inf. Comput. Sci. 2004, 44, 2179-2189.
23. Sutherland, J.; O'Brien, L.; Weaver, D. Spline-Fitting with a Genetic Algorithm: A Method for Developing Classification Structure-Activity Relationships. J. Chem. Inf. Model. 2003, 43, 1906-1915.
24. Chemical Computing Group Inc. Molecular Operating Environment (MOE 2004.03); Chemical Computing Group: Montreal, Quebec, Canada.
25. Kaufman, L.; Rousseeuw, P. Finding Groups in Data: An Introduction to Cluster Analysis; Wiley: New York, 1990.
26. MDL Information Systems Inc.
27. Kier, L.; Hall, L. Molecular Connectivity in Structure Activity Analysis; John Wiley & Sons: Hertfordshire, England, 1986.
28. Randic, M. On Molecular Idenitification Numbers. J. Chem. Inf. Comput. Sci. 1984, 24, 164-175.
29. Liu, S.; Cao, C.; Li, Z. Approach to Estimation and Prediction For Normal Boiling Point (NBP) of Alkanes Based on a Novel Molecular Distance Edge (MDE) Vector, λ. J. Chem. Inf. Comput. Sci. 1998, 38, 387-394.
30. Stanton, D.; Mattioni, B. E.; Knittel, J.; Jurs, P. Development and Use of Hydrophobic Surface Area (HSA) Descriptors for Computer Assisted Quantitative Structure-Activity and Structure-Property Relationships. J. Chem. Inf. Comput. Sci. 2004, 44, 1010-1023.
31. Mattioni, B. E. The Development of Quantitative Structure-Activity Relationship Mode Is for Physical Property and Biological Activity Prediction of Organic Compounds. Ph.D. Thesis, Pennsylvania State University, University Park, PA, 2003.
32. Wildman, S.; Grippen, G. Prediction of Physiochemical Parameters by Atomic Contributions. J. Chem. Inf. Comput. Sci. 1999, 39, 868-873.