126 related articles for article (PubMed ID: 16426050)
1. Supervised self-organizing maps in drug discovery. 2. Improvements in descriptor selection and model validation.
Xiao YD; Harris R; Bayram E; Ii PS; Schmitt JD
J Chem Inf Model; 2006; 46(1):137-44. PubMed ID: 16426050
[TBL] [Abstract][Full Text] [Related]
2. Genetic algorithms and self-organizing maps: a powerful combination for modeling complex QSAR and QSPR problems.
Bayram E; Santago P; Harris R; Xiao YD; Clauset AJ; Schmitt JD
J Comput Aided Mol Des; 2004; 18(7-9):483-93. PubMed ID: 15729848
[TBL] [Abstract][Full Text] [Related]
3. Generation of QSAR sets with a self-organizing map.
Guha R; Serra JR; Jurs PC
J Mol Graph Model; 2004 Sep; 23(1):1-14. PubMed ID: 15331049
[TBL] [Abstract][Full Text] [Related]
4. Supervised self-organizing maps in drug discovery. 1. Robust behavior with overdetermined data sets.
Xiao YD; Clauset A; Harris R; Bayram E; Santago P; Schmitt JD
J Chem Inf Model; 2005; 45(6):1749-58. PubMed ID: 16309281
[TBL] [Abstract][Full Text] [Related]
5. Applications of self-organizing neural networks in virtual screening and diversity selection.
Selzer P; Ertl P
J Chem Inf Model; 2006; 46(6):2319-23. PubMed ID: 17125175
[TBL] [Abstract][Full Text] [Related]
6. Combinatorial QSAR modeling of specificity and subtype selectivity of ligands binding to serotonin receptors 5HT1E and 5HT1F.
Wang XS; Tang H; Golbraikh A; Tropsha A
J Chem Inf Model; 2008 May; 48(5):997-1013. PubMed ID: 18470978
[TBL] [Abstract][Full Text] [Related]
7. Ensembles of Bayesian-regularized genetic neural networks for modeling of acetylcholinesterase inhibition by huprines.
Fernández M; Caballero J
Chem Biol Drug Des; 2006 Oct; 68(4):201-12. PubMed ID: 17105484
[TBL] [Abstract][Full Text] [Related]
8. Statistical external validation and consensus modeling: a QSPR case study for Koc prediction.
Gramatica P; Giani E; Papa E
J Mol Graph Model; 2007 Mar; 25(6):755-66. PubMed ID: 16890002
[TBL] [Abstract][Full Text] [Related]
9. Stochastic versus stepwise strategies for quantitative structure-activity relationship generation--how much effort may the mining for successful QSAR models take?
Horvath D; Bonachera F; Solov'ev V; Gaudin C; Varnek A
J Chem Inf Model; 2007; 47(3):927-39. PubMed ID: 17480052
[TBL] [Abstract][Full Text] [Related]
10. QSPR modeling of soil sorption coefficients (K(OC)) of pesticides using SPA-ANN and SPA-MLR.
Goudarzi N; Goodarzi M; Araujo MC; Galvão RK
J Agric Food Chem; 2009 Aug; 57(15):7153-8. PubMed ID: 19722589
[TBL] [Abstract][Full Text] [Related]
11. In silico prediction of mitochondrial toxicity by using GA-CG-SVM approach.
Zhang H; Chen QY; Xiang ML; Ma CY; Huang Q; Yang SY
Toxicol In Vitro; 2009 Feb; 23(1):134-40. PubMed ID: 18940245
[TBL] [Abstract][Full Text] [Related]
12. QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation.
Votano JR; Parham M; Hall LM; Hall LH; Kier LB; Oloff S; Tropsha A
J Med Chem; 2006 Nov; 49(24):7169-81. PubMed ID: 17125269
[TBL] [Abstract][Full Text] [Related]
13. Predicting antitrichomonal activity: a computational screening using atom-based bilinear indices and experimental proofs.
Marrero-Ponce Y; Meneses-Marcel A; Castillo-Garit JA; Machado-Tugores Y; Escario JA; Barrio AG; Pereira DM; Nogal-Ruiz JJ; Arán VJ; Martínez-Fernández AR; Torrens F; Rotondo R; Ibarra-Velarde F; Alvarado YJ
Bioorg Med Chem; 2006 Oct; 14(19):6502-24. PubMed ID: 16875830
[TBL] [Abstract][Full Text] [Related]
14. Anticancer activity of selected phenolic compounds: QSAR studies using ridge regression and neural networks.
Nandi S; Vracko M; Bagchi MC
Chem Biol Drug Des; 2007 Nov; 70(5):424-36. PubMed ID: 17949360
[TBL] [Abstract][Full Text] [Related]
15. Modeling of cyclin-dependent kinase inhibition by 1H-pyrazolo[3,4-d]pyrimidine derivatives using artificial neural network ensembles.
Fernández M; Tundidor-Camba A; Caballero J
J Chem Inf Model; 2005; 45(6):1884-95. PubMed ID: 16309296
[TBL] [Abstract][Full Text] [Related]
16. Quantitative structure-retention relationship for the Kovats retention indices of a large set of terpenes: a combined data splitting-feature selection strategy.
Hemmateenejad B; Javadnia K; Elyasi M
Anal Chim Acta; 2007 May; 592(1):72-81. PubMed ID: 17499073
[TBL] [Abstract][Full Text] [Related]
17. Modeling robust QSAR.
Polanski J; Bak A; Gieleciak R; Magdziarz T
J Chem Inf Model; 2006; 46(6):2310-8. PubMed ID: 17125174
[TBL] [Abstract][Full Text] [Related]
18. In silico ADME modelling 2: computational models to predict human serum albumin binding affinity using ant colony systems.
Gunturi SB; Narayanan R; Khandelwal A
Bioorg Med Chem; 2006 Jun; 14(12):4118-29. PubMed ID: 16504519
[TBL] [Abstract][Full Text] [Related]
19. QSAR modeling of mono- and bis-quaternary ammonium salts that act as antagonists at neuronal nicotinic acetylcholine receptors mediating dopamine release.
Zheng F; Bayram E; Sumithran SP; Ayers JT; Zhan CG; Schmitt JD; Dwoskin LP; Crooks PA
Bioorg Med Chem; 2006 May; 14(9):3017-37. PubMed ID: 16431111
[TBL] [Abstract][Full Text] [Related]
20. Prediction of HPLC retention index using artificial neural networks and IGroup E-state indices.
Albaugh DR; Hall LM; Hill DW; Kertesz TM; Parham M; Hall LH; Grant DF
J Chem Inf Model; 2009 Apr; 49(4):788-99. PubMed ID: 19309176
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]