FieldChopper, a new tool for automatic model generation and virtual screening based on molecular fields

Journal of Chemical Information and Modeling

Volumn 48, Issue 6, 2008, Pages 1131-1137

  Kalliokoski, Tuomo ^a   Ronkko, Toni ^a   Poso, Antti ^a  

^a UNIVERSITY OF EASTERN FINLAND   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTROSTATICS; MOLECULAR SIEVES; VAN DER WAALS FORCES;

ACTIVE MOLECULES; AUTOMATIC MODEL GENERATION; DISCRETIZATIONS; FIELD VALUES; MOLECULAR DATABASE; MOLECULAR FIELDS; VAN DER WAALS; VIRTUAL SCREENING;

LIGANDS;

LIGAND;

ALGORITHM; AREA UNDER THE CURVE; ARTICLE; COMPUTER PROGRAM; COMPUTER SIMULATION; DRUG SCREENING; ELECTRICITY; FACTUAL DATABASE; METHODOLOGY; REPRODUCIBILITY; ROC CURVE;

ALGORITHMS; AREA UNDER CURVE; COMPUTER SIMULATION; DATABASES, FACTUAL; DRUG EVALUATION, PRECLINICAL; ELECTROSTATICS; LIGANDS; REPRODUCIBILITY OF RESULTS; ROC CURVE; SOFTWARE;

EID: 47349106302     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci700216u     Document Type: Article

References (49)

1. Willett, P. Similarity-based virtual screening using 2D fingerprints. Drug Discovery Today 2006, 11, 1046-1053.
2. Jorgensen, W. L. The many roles of computation in drug discovery. Science 2004, 303, 1813-1818.
3. Kontoyianni, M.; Sokol, G. S.; McClellan, L. M. Evaluation of library ranking efficacy in virtual screening. J. Comput. Chem. 2005, 26, 11-22.
4. Kitchen, D. B.; Decornez, H.; Furr, J. R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discovery 2004, 3, 935-949.
5. Warren, G. L.; Andrews, C. W.; Capelli, A. M.; Clarke, B.; LaLonde, J.; Lambert, M. H.; Lindvall, M.; Semus, S. F.; Senger, S.; Tedesco, G.; Wall, I. D.; Woolven, J. M.; Peishoff, C. E.; Head, M. S. Critical assessment of docking programs and scoring functions. J. Med. Chem. 2006, 49, 5912-5931.
6. Todeschini, R.; Consonni, V. Handbook to molecular descriptors. In Methods and Principles in Medicinal Chemistry; Mannhold, R., Kybinyi, H., Folkers, G., Eds.; Wiley-VCH: Weinheim, Germany, 2000; Vol. 11.
7. Grant, J. A.; Gallard, M. A.; Pickup, B. T. A fast method of molecular shape comparison: a simple application of a Gaussian description of molecular shape. J. Comput. Chem. 1996, 17, 1653-1666.
8. Nicholls, A.; MacGuish, N. E.; MacGuish, J. D. Variable selection and model validation of 2D and 3D molecular descriptors. J. Comput-Aided Mol. Des. 2004, 18, 451-474.
9. Melani, F.; Gratteri, P.; Adamo, M.; Bonaccini, C. Field interaction and geometrical overlap: A new simplex and experimental design based computational procedure for superposing small ligand molecules. J. Med. Chem. 2003, 46, 1359-1371.
10. Cheeseright, T.; Mackey, M.; Rose, S.; Vinter, A. Molecular field extrema as descriptors of biological activity: definition and validation. J. Chem. Inf. Model. 2006, 46, 665-676.
11. Wolber, G.; Dornhofer, A. A.; Langer, T. Efficient overlay of small organic molecules using 3D pharmacophores. J. Comput.-Aided Mol. Des. 2006, 20, 773-788.
12. Ronkko, T.; Tervo, A. J.; Parkkinen, J.; Poso, A. BRUTUS: optimization of a grid-based similarity function for rigid-body molecular superposition. II. Description and characterization. J. Comput.-Aided Mol. Des. 2006, 20, 227-236.
13. Dixon, S. L.; Smondyrev, A. M.; Knoll, E. H.; Rao, S. N.; Shaw, D. E.; Friesner, R. A. PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J. Comput.-Aided Mol. Des. 2006, 20, 647-671.
14. Langer, T.; Hoffman, R. D. Pharmacophores and Pharmacophore Searches. In Methods and Principles in Medicinal Chemistry; Mannhold, R., Kubinyi, H., Folkers, G., Eds.; Wiley-VCH: Weinheim, Germany, 2006; Vol. 32.
15. Wolber, G.; Langer, T. LigandScout: 3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use as Virtual Screening Filters. J. Chem. Inf. Model. 2004, 45, 160-169.
16. Chen, J.; Lai, L. Pocket v.2: Further developments on receptor-based pharmacophore modeling. J. Chem. Inf. Model. 2006, 46, 2684-2691.
17. Ortuso, F.; Langer, T.; Alcaro, S. GBPM: GRID-based pharmacophore model: concept and application studies to protein-protein recognition. Bioinformatics 2006, 22, 1449-1455.
18. Cramer, R. D., III; 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.
19. Putta, S.; Lemmen, C.; Beroza, P.; Greene, J. A novel shape-feature based approach to virtual screening. J. Chem. Inf. Comput. Sci. 2002, 42, 1230-1240.
20. Jain, A. N. Ligand-based structural hypotheses for virtual screening. J. Med. Chem. 2004, 47, 947-961.
21. Kotani, T.; Higashiura, K. Comparative Molecular Active Site Analysis (CoMASA). 1. An approach to rapid evaluation of 3D QSAR. J. Med. Chem. 2004, 47, 2732-2742.
22. ROCS, version 2.3; OpenEye Scientific Software, Inc.: Santa Fe, NM.
23. Tervo, A. J.; Ronkko, T.; Nyronen, T. H.; Poso, A. BRUTUS: optimization of a grid-based similarity function for rigid-body molecular superposition. 1. Alignment and virtual screening applications. J. Med. Chem. 2005, 48, 4076-4086.
24. Dill, K. A.; Bromberg, S. Molecular driving forces, 1st ed.; Garland Science: New York, NY, 2002; pp 370-371.
25. Huang, N.; Shoichet, B. K.; Irwin, J. J. Benchmarking Sets for Molecular Docking. J. Med. Chem. 2006, 49, 6789-6801.
26. OMEGA, version 2.1; OpenEye Scientific Software, Inc.: Santa Fe, NM.
27. MolCharge, version 1.3.1; OpeneEye Scientific Software, Inc.: Santa Fe, NM.
28. Jennings, A.; Tennant, M. Selection of molecules based on shape and electrostatic similarity: proof of concept of "Electroforms". J. Chem. Inf. Model. 2007, 47, 1829-1838.
29. Hodgkin, E. E.; Richards, W. G. Molecular similarity based on electrostatic potential and electric field. Int. J. Quantum Chem., Quantum Biol. Symp. 1987, 14, 105-110.
30. Berman, H. M.; Battistuz, T.; Bhat, T. N.; Bluhm, W. F.; Bourne, P. E.; Burkhardt, K.; Feng, Z.; Gilliland, G. L.; Iype, L.; Jain, S; Fagan, P.; Marvin, J.; Padilla, D.; Ravichandran, V.; Schneider, B.; Thanki, N.; Weissig, H.; Westbrook, J. D.; Zardecki, C. The Protein Data Bank. Nucleic Acids Res. 2000, 28, 235-242.
31. SYBYL, version 7.1; Tripos, Inc.: St. Louis, MO.
32. EON, version 2.0.1; OpenEye Scientific Software, Inc.: Santa Fe, NM.
33. Muchmore, S. W.; Souers, A. J.; Akritopoulou-Zanze, I. The use of three-dimensional shape and electrostatic similarity searching in the identification of a melanin-concentrating hormone receptor 1 antagonist. Chem. Biol. Drug Des. 2006, 67, 174-176.
34. Pastor, M.; Cruciani, G.; McLay, I.; Pickett, S.; Clementi, S. GRidINdependent Descriptors (GRIND): A novel class of alignment-independent three-dimensional molecular descriptors. J. Med. Chem. 2000, 43, 3233-3243.
35. ALMOND, version 3.3; Molecular Discovery, Ltd.: Perugia, Italy.
36. Daszykowski, M.; Walczak, B.; Massart, D. L. Representative subset selection. Anal. Chim. Acta 2002, 468, 91-103.
37. GNU Octave, version 2.1.73. http://www.gnu.org/software/octave (accessed Oct 16, 2007).
38. Chen, H.; Lyne, P. D.; Giordanetto, F.; Lovell, T.; Li, J. On evaluating molecular-docking methods for pose prediction and enrichment factors. J. Chem. Inf. Model. 2006, 46, 401-415.
39. Hawkins, P. C.; Skillman, A. G.; Nicholls, A. Comparison of shape-matching and docking as virtual screening tools. J. Med. Chem. 2007, 50, 74-82.
40. McGaughey, G. B.; Sheridan, R. P.; Bayly, C. I.; Culberson, J. C.; Kreatsoulas, C.; Lindsley, S.; Maiorov, V.; Truchon, J.-F.; Cornell, W. D. Comparison of topological, shape, and docking methods in virtual screening. J. Chem. Inf. Model. 2007, 47, 1504-1519.
41. Triballeau, N.; Acher, F.; Brabet, I.; Pin, J.-P.; Bertrand, H.-O. Virtual screening workflow development guided by the "Receiver Operating Characteristic" Curve Approach. Application to High-Throughput Docking on Metabotropic Glutamate Receptor Subtype 4. J. Med. Chem. 2005, 48, 2534-2537.
42. Sing, T.; Sander, O.; Beerenwinkel, N.; Lengauer, T. ROCR: visualizing classifier performance in R. Bioinformatics 2006, 21, 3940-3941.
43. Demsar, J. Statistical comparisons on classifiers over multiple data sets. J. Mach. Learn. Res. 2006, 7, 1-30.
44. R, version 2.6.0. http://www.r-project.org (accessed Nov 8, 2007).
45. Truchon, J.-F.; Bayly, C. I. Evaluating virtual screening methods: good and bad metrics for the "Early recognition" problem. J. Chem. Inf. Model. 2007, 47, 488-508.
46. Doweyko, A. M. 3D-QSAR illusions. J. Comput.-Aided Mol. Des. 2004, 18, 587-596.
47. Norinder, U.; Bergstrom, C. A. S. Prediction of ADMET properties. ChemMedChem 2006, 1, 920-937.
48. Lobell, M.; Hendrix, M.; Hinzen, B.; Keldenich, J.; Meier, H.; Schmeck, C.; Schohe-Loop, R.; Wunberg, T.; Hillisch, A. In silico ADMET traffic lights as a tool for the prioritization of HTS hits. ChemMedChem 2006, 1, 1229-1236.
49. Crivori, P.; Cruciani, G.; Carrupt, P.-A.; Testa, B. Predicting bloodbrain barrier permeation from three-dimensional molecular structure. J. Med. Chem. 2000, 43, 2204-2216.