3-D QSAutogrid/R: An alternative procedure to build 3-D QSAR models. methodologies and applications

Journal of Chemical Information and Modeling

Volumn 52, Issue 6, 2012, Pages 1674-1685

  Ballante, Flavio ^a   Ragno, Rino ^a  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

MOLECULAR GRAPHICS;

ACTIVITY PREDICTIONS; ALTERNATIVE PROCEDURES; CORE ENGINES; DRUG DESIGN; MOLECULAR DATA; MOLECULAR INTERACTION FIELDS; MOLECULAR INTERFACES; VARIABLE SELECTION;

COMPUTATIONAL CHEMISTRY;

LIGAND; OPIATE RECEPTOR;

ARTICLE; CHEMICAL STRUCTURE; COMPUTER GRAPHICS; COMPUTER PROGRAM; DRUG ANTAGONISM; QUANTITATIVE STRUCTURE ACTIVITY RELATION; VALIDATION STUDY;

COMPUTER GRAPHICS; LIGANDS; MODELS, MOLECULAR; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; RECEPTORS, OPIOID; SOFTWARE;

EID: 84862892418     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci300123x     Document Type: Article

References (43)

1. Tosco, P.; Balle, T. Open3DQSAR: a new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields J. Mol. Model. 2011, 17, 201-208
2. Cramer, R. D.; Patterson, D. E.; Bunce, J. D. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins J. Am. Chem. Soc. 1988, 110, 5959-5967
3. Goodford, P. J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules J. Med. Chem. 1985, 28, 849-857
4. Baroni, M.; Costantino, G.; Cruciani, G.; Riganelli, D.; Valigi, R.; Clementi, S. Generating Optimal Linear Pls Estimations (Golpe)-an Advanced Chemometric Tool for Handling 3d-Qsar Problems Quant. Struct.-Act. Relat. 1993, 12, 9-20
5. Cruciani, G.; Watson, K. A. Comparative Molecular-Field Analysis Using Grid Force-Field and Golpe Variable Selection Methods in a Study of Inhibitors of Glycogen-Phosphorylase-B J. Med. Chem. 1994, 37, 2589-2601
6. Verma, J.; Khedkar, V. M.; Coutinho, E. C. 3D-QSAR in drug design - a review. Curr. Top. Med. Chem. 10, 95-115.
7. Sippl, W. 3D-QSAR-Applications, Recent Advances, and Limitations. In Recent Advances in QSAR Studies; Puzyn, T.; Leszczynski, J.; Cronin, M. T., Eds.; Springer: Netherlands: Vol. 8, pp 103-125.
8. Team, R. D. C. The R Foundation for Statistical Computing. http://www.r-project.org/ (accessed month day, year).
9. Morris, G. M.; Huey, R.; Lindstrom, W.; Sanner, M. F.; Belew, R. K.; Goodsell, D. S.; Olson, A. J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility J. Comput. Chem. 2009, 30, 2785-2791
10. The University of California, S. F. U. UCSF chimera. http://www.cgl.ucsf. edu/chimera/ (accessed month day, year).
11. Python Molecular Viewer (PMV). Molecular Graphics Laboratory, T. S. R. I. http://mgltools.scripps.edu/ (accessed month day, year).
12. AutoDockTools (ADT). Molecular Graphics Laboratory, T. S. R. I. http://mgltools.scripps.edu/ (accessed month day, year).
13. Willighagen, E. genalg: R Based Genetic Algorithm, 0.1.1.; 2005.
14. Cho, S. J.; Tropsha, A. Cross-validated R2-guided region selection for comparative molecular field analysis: a simple method to achieve consistent results J. Med. Chem. 1995, 38, 1060-1066
15. Cho, S. J.; Tropsha, A.; Suffness, M.; Cheng, Y. C.; Lee, K. H. Antitumor agents. 163. Three-dimensional quantitative structure-activity relationship study of 4′-O-demethylepipodophyllotoxin analogs using the modified CoMFA/q2-GRS approach J. Med. Chem. 1996, 39, 1383-1395
16. Cruciani, G.; Clementi, S.; Pastor, M. GOLPE-guided region selection Perspect. Drug Discovery Des. 1998, 12-14, 71-86
17. Peng, Y.; Keenan, S. M.; Zhang, Q.; Kholodovych, V.; Welsh, W. J. 3D-QSAR comparative molecular field analysis on opioid receptor antagonists: pooling data from different studies J. Med. Chem. 2005, 48, 1620-1629
18. Musmuca, I.; Caroli, A.; Mai, A.; Kaushik-Basu, N.; Arora, P.; Ragno, R. Combining 3-D quantitative structure-activity relationship with ligand based and structure based alignment procedures for in silico screening of new hepatitis C virus NS5B polymerase inhibitors J. Chem. Inf. Model. 2010, 50, 662-676
19. Wold, S.; Ruhe, A.; Wold, H.; Dunn, W. J., III. The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses SIAM J. Sci. Stat. Comput. 1984, 5, 735-743
20. Clark, M.; Cramer, R. D. The Probability of Chance Correlation Using Partial Least-Squares (Pls) Quant. Struct.-Act. Relat. 1993, 12, 137-145
21. Cruciani, G. Molecular interaction fields: applications in drug discovery and ADME prediction; Wiley-VCH: Weinheim, 2006; p xviii, 307 p.
22. Wold, S.; Johansson, E.; Cocchi, M. PLS: Partial Least Squares Projections to Latent Structures in 3D QSAR in Drug Design: Theory Methods and Applications; ESCOM Science Publishers: 1993.
23. Mitchell, T. J. An algorithm for the construction of ″D- optimal″ experimental designs Technometrics 2000, 42, 48-54
24. Box, G. E. P.; Hunter, W. G.; Hunter, J. S.; Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building; John Wiley & Sons: 1978; p 653.
25. Kirkpatrick, S.; Gelatt, C. D., Jr.; Vecchi, M. P. Optimization by simulated annealing Science 1983, 220, 671-680
26. Holland, J. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence; A Bradford Book: 1992.
27. Baroni, M.; Clementi, S.; Cruciani, G.; Kettanehwold, N.; Wold, S. D-Optimal Designs in Qsar Quant. Struct.-Act. Relat. 1993, 12, 225-231
28. Puzyn, T.; Leszczynski, J.; Cronin, M. T. D. Recent advances in QSAR studies: methods and applications; Springer: Dordrecht; New York, p xiv, 423 p.
29. Shen, M.; LeTiran, A.; Xiao, Y. D.; Golbraikh, A.; Kohn, H.; Tropsha, A. Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant-agents using k nearest neighbor and simulated annealing PLS methods J. Med. Chem. 2002, 45, 2811-2823
30. Kubinyi, H.; Folkers, G.; Martin, Y. C. 3D QSAR in drug design; Kluwer Academic: Dordrecht; Boston, MA, 1998; p v. < 2- >.
31. Pastor, M.; Cruciani, G.; Clementi, S. Smart region definition: a new way to improve the predictive ability and interpretability of three-dimensional quantitative structure-activity relationships J. Med. Chem. 1997, 40, 1455-1464
32. Cho, S. J.; Tropsha, A. Cross-validated R2-guided region selection for comparative molecular field analysis: a simple method to achieve consistent results J. Med. Chem. 1995, 38, 1060-1066
33. Wold, S.; Eriksson, L. Chemometrics Methods in Molecular Design; VCH: Weinheim: 1995.
34. Crawley, M. J. The R book; Wiley: Chichester, England; Hoboken, NJ, 2007; p viii, 942 p.
35. Wickham, H. ggplot2: elegant graphics for data analysis; Springer: New York, 2009.
36. Jmol: an open-source Java viewer for chemical structures in 3D.
37. Cruciani, G.; Clementi, S.; Baroni, M. Variable Selection in PLS Analysis. In 3D QSAR in Drug Design; Kubinyi, H., Ed.; ESCOM: Leiden, 1993; pp 551-564.
38. Tedesco, R.; Shaw, A. N.; Bambal, R.; Chai, D.; Concha, N. O.; Darcy, M. G.; Dhanak, D.; Fitch, D. M.; Gates, A.; Gerhardt, W. G.; Halegoua, D. L.; Han, C.; Hofmann, G. A.; Johnston, V. K.; Kaura, A. C.; Liu, N.; Keenan, R. M.; Lin-Goerke, J.; Sarisky, R. T.; Wiggall, K. J.; Zimmerman, M. N.; Duffy, K. J. 3-(1,1-dioxo-2H-(1,2,4)-benzothiadiazin-3-yl)-4-hydroxy-2(1H)-quinolinones, potent inhibitors of hepatitis C virus RNA-dependent RNA polymerase J. Med. Chem. 2006, 49, 971-983
39. Li, T.; Froeyen, M.; Herdewijn, P. Insight into ligand selectivity in HCV NS5B polymerase: molecular dynamics simulations, free energy decomposition and docking J. Mol. Model. 2010, 16, 49-59
40. Ryu, K.; Kim, N. D.; Il Choi, S.; Han, C. K.; Yoon, J. H.; No, K. T.; Kim, K. H.; Seong, B. L. Identification of novel inhibitors of HCV RNA-dependent RNA polymerase by pharmacophore-based virtual screening and in vitro evaluation Bioorg. Med. Chem. 2009, 17, 2975-2982
41. Ballante, F.; Musmuca, I.; Patsilinakos, A.; Ragno, R. An Alternative Method for Generating 3-D QSAR Models using Free Software. In 5th Joint Sheffield Conference on Chemoinformatics; Sheffield, UK, 2010.
42. Patsilinakos, A.; Ballante, F.; Musmuca, I.; Ragno, R. 3-D QSAR SERVER - A 3-D QSAR Models Database for Virtual Screening. In 14th Hellenic Symposium on Medicinal Chemistry; Thessaloniki, Greece, 2010.
43. Musmuca, I.; Ballante, F.; Ragno, R. R/AUTOGRID/ADT Combination As An Alternative To Build 3-D QSAR Models. Methodologies And Applications. In 18th European Symposium on Quantitative Structure-Activity Relationships; Rhodes, Greece, 2010.