150 related articles for article (PubMed ID: 16421721)
1. A radial-distribution-function approach for predicting rodent carcinogenicity.
Morales AH; Cabrera Pérez MA; González MP
J Mol Model; 2006 Sep; 12(6):769-80. PubMed ID: 16421721
[TBL] [Abstract][Full Text] [Related]
2. A topological substructural approach applied to the computational prediction of rodent carcinogenicity.
Helguera AM; Cabrera Pérez MA; González MP; Ruiz RM; González Díaz H
Bioorg Med Chem; 2005 Apr; 13(7):2477-88. PubMed ID: 15755650
[TBL] [Abstract][Full Text] [Related]
3. A radial distribution function approach to predict A(2B) agonist effect of adenosine analogues.
González MP; Terán C; Fall Y; Teijeira M; Besada P
Bioorg Med Chem; 2005 Feb; 13(3):601-8. PubMed ID: 15653328
[TBL] [Abstract][Full Text] [Related]
4. Radial distribution function descriptors: an alternative for predicting A2 A adenosine receptors agonists.
González MP; Terán C; Teijeira M; Helguera AM
Eur J Med Chem; 2006 Jan; 41(1):56-62. PubMed ID: 16253394
[TBL] [Abstract][Full Text] [Related]
5. Predicting carcinogenicity and understanding the carcinogenic mechanism of N-nitroso compounds using a TOPS-MODE approach.
Yuan J; Pu Y; Yin L
Chem Res Toxicol; 2011 Dec; 24(12):2269-79. PubMed ID: 22084901
[TBL] [Abstract][Full Text] [Related]
6. A TOPS-MODE approach to predict adenosine kinase inhibition.
González MP; Moldes del Carmen Terán M
Bioorg Med Chem Lett; 2004 Jun; 14(12):3077-9. PubMed ID: 15149648
[TBL] [Abstract][Full Text] [Related]
7. Computer-aided rodent carcinogenicity prediction.
Lagunin AA; Dearden JC; Filimonov DA; Poroikov VV
Mutat Res; 2005 Oct; 586(2):138-46. PubMed ID: 16112600
[TBL] [Abstract][Full Text] [Related]
8. Quantitative structure activity relationship for the computational prediction of nitrocompounds carcinogenicity.
Morales AH; Pérez MA; Combes RD; González MP
Toxicology; 2006 Mar; 220(1):51-62. PubMed ID: 16414170
[TBL] [Abstract][Full Text] [Related]
9. QSAR study for carcinogenicity in a large set of organic compounds.
Duchowicz PR; Comelli NC; Ortiz EV; Castro EA
Curr Drug Saf; 2012 Sep; 7(4):282-8. PubMed ID: 23062240
[TBL] [Abstract][Full Text] [Related]
10. Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling.
Valerio LG; Arvidson KB; Chanderbhan RF; Contrera JF
Toxicol Appl Pharmacol; 2007 Jul; 222(1):1-16. PubMed ID: 17482223
[TBL] [Abstract][Full Text] [Related]
11. Quantitative structure carcinogenicity relationship for detecting structural alerts in nitroso-compounds.
Helguera AM; González MP; D S Cordeiro MN; Pérez MA
Toxicol Appl Pharmacol; 2007 Jun; 221(2):189-202. PubMed ID: 17477948
[TBL] [Abstract][Full Text] [Related]
12. QSAR prediction of rodent carcinogenicity for a set of chemicals currently bioassayed by the US National Toxicology Program.
Benigni R
Mutagenesis; 1991 Sep; 6(5):423-5. PubMed ID: 1795649
[TBL] [Abstract][Full Text] [Related]
13. Development of classification and regression based QSAR models to predict rodent carcinogenic potency using oral slope factor.
Kar S; Deeb O; Roy K
Ecotoxicol Environ Saf; 2012 Aug; 82():85-95. PubMed ID: 22698880
[TBL] [Abstract][Full Text] [Related]
14. Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches.
Singh KP; Gupta S; Rai P
Toxicol Appl Pharmacol; 2013 Oct; 272(2):465-75. PubMed ID: 23856075
[TBL] [Abstract][Full Text] [Related]
15. Development of quantitative structure-activity relationship (QSAR) models to predict the carcinogenic potency of chemicals I. Alternative toxicity measures as an estimator of carcinogenic potency.
Venkatapathy R; Wang CY; Bruce RM; Moudgal C
Toxicol Appl Pharmacol; 2009 Jan; 234(2):209-21. PubMed ID: 18977375
[TBL] [Abstract][Full Text] [Related]
16. Predicting the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices.
Contrera JF; Matthews EJ; Daniel Benz R
Regul Toxicol Pharmacol; 2003 Dec; 38(3):243-59. PubMed ID: 14623477
[TBL] [Abstract][Full Text] [Related]
17. Prediction of chemical carcinogenicity by machine learning approaches.
Tan NX; Rao HB; Li ZR; Li XY
SAR QSAR Environ Res; 2009; 20(1-2):27-75. PubMed ID: 19343583
[TBL] [Abstract][Full Text] [Related]
18. QSAR studies about cytotoxicity of benzophenazines with dual inhibition toward both topoisomerases I and II: 3D-MoRSE descriptors and statistical considerations about variable selection.
Saíz-Urra L; González MP; Teijeira M
Bioorg Med Chem; 2006 Nov; 14(21):7347-58. PubMed ID: 16962784
[TBL] [Abstract][Full Text] [Related]
19. QSAR modeling of carcinogenic risk using discriminant analysis and topological molecular descriptors.
Contrera JF; Maclaughlin P; Hall LH; Kier LB
Curr Drug Discov Technol; 2005 Jun; 2(2):55-67. PubMed ID: 16472230
[TBL] [Abstract][Full Text] [Related]
20. Quantitative structure-carcinogenicity relationship for detecting structural alerts in nitroso compounds: species, rat; sex, female; route of administration, gavage.
Morales Helguera A; Pérez González M; Dias Soeiro Cordeiro MN; Cabrera Pérez MA
Chem Res Toxicol; 2008 Mar; 21(3):633-42. PubMed ID: 18293904
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]