128 related articles for article (PubMed ID: 38047445)
41. Integration of structure-activity relationship and artificial intelligence systems to improve in silico prediction of ames test mutagenicity.
Mazzatorta P; Tran LA; Schilter B; Grigorov M
J Chem Inf Model; 2007; 47(1):34-8. PubMed ID: 17238246
[TBL] [Abstract][Full Text] [Related]
42. AMPred-CNN: Ames mutagenicity prediction model based on convolutional neural networks.
Tran TTV; Tayara H; Chong KT
Comput Biol Med; 2024 Jun; 176():108560. PubMed ID: 38754218
[TBL] [Abstract][Full Text] [Related]
43. Three new consensus QSAR models for the prediction of Ames genotoxicity.
Votano JR; Parham M; Hall LH; Kier LB; Oloff S; Tropsha A; Xie Q; Tong W
Mutagenesis; 2004 Sep; 19(5):365-77. PubMed ID: 15388809
[TBL] [Abstract][Full Text] [Related]
44. Prediction of aromatic amines mutagenicity from theoretical molecular descriptors.
Gramatica P; Consonni V; Pavan M
SAR QSAR Environ Res; 2003 Aug; 14(4):237-50. PubMed ID: 14506868
[TBL] [Abstract][Full Text] [Related]
45. An evaluation of in-house and off-the-shelf in silico models: implications on guidance for mutagenicity assessment.
Jolly R; Ahmed KB; Zwickl C; Watson I; Gombar V
Regul Toxicol Pharmacol; 2015 Apr; 71(3):388-97. PubMed ID: 25656493
[TBL] [Abstract][Full Text] [Related]
46. Use of orbitrap-MS/MS and QSAR analyses to estimate mutagenic transformation products of iopamidol generated during ozonation and chlorination.
Matsushita T; Hashizuka M; Kuriyama T; Matsui Y; Shirasaki N
Chemosphere; 2016 Apr; 148():233-40. PubMed ID: 26807944
[TBL] [Abstract][Full Text] [Related]
47. Identification of the structural requirements for mutagenicity by incorporating molecular flexibility and metabolic activation of chemicals I: TA100 model.
Mekenyan O; Dimitrov S; Serafimova R; Thompson E; Kotov S; Dimitrova N; Walker JD
Chem Res Toxicol; 2004 Jun; 17(6):753-66. PubMed ID: 15206896
[TBL] [Abstract][Full Text] [Related]
48. QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction.
Hung C; Gini G
Mol Divers; 2021 Aug; 25(3):1283-1299. PubMed ID: 34146224
[TBL] [Abstract][Full Text] [Related]
49. Identification of mutagenic transformation products generated during oxidation of 3-methyl-4-nitrophenol solutions by orbitrap tandem mass spectrometry and quantitative structure-activity relationship analyses.
Matsushita T; Honda S; Kuriyama T; Fujita Y; Kondo T; Matsui Y; Shirasaki N; Takanashi H; Kameya T
Water Res; 2018 Feb; 129():347-356. PubMed ID: 29169108
[TBL] [Abstract][Full Text] [Related]
50. Bacterial mutagenicity test data: collection by the task force of the Japan pharmaceutical manufacturers association.
Hakura A; Awogi T; Shiragiku T; Ohigashi A; Yamamoto M; Kanasaki K; Oka H; Dewa Y; Ozawa S; Sakamoto K; Kato T; Yamamura E
Genes Environ; 2021 Sep; 43(1):41. PubMed ID: 34593056
[TBL] [Abstract][Full Text] [Related]
51. Merging applicability domains for in silico assessment of chemical mutagenicity.
Liu R; Wallqvist A
J Chem Inf Model; 2014 Mar; 54(3):793-800. PubMed ID: 24494696
[TBL] [Abstract][Full Text] [Related]
52. In silico prediction of the mutagenicity of nitroaromatic compounds using a novel two-QSAR approach.
Ding YL; Lyu YC; Leong MK
Toxicol In Vitro; 2017 Apr; 40():102-114. PubMed ID: 28027902
[TBL] [Abstract][Full Text] [Related]
53. In silico prediction of chemical Ames mutagenicity.
Xu C; Cheng F; Chen L; Du Z; Li W; Liu G; Lee PW; Tang Y
J Chem Inf Model; 2012 Nov; 52(11):2840-7. PubMed ID: 23030379
[TBL] [Abstract][Full Text] [Related]
54. Prediction on the mutagenicity of nitroaromatic compounds using quantum chemistry descriptors based QSAR and machine learning derived classification methods.
Hao Y; Sun G; Fan T; Sun X; Liu Y; Zhang N; Zhao L; Zhong R; Peng Y
Ecotoxicol Environ Saf; 2019 Dec; 186():109822. PubMed ID: 31634658
[TBL] [Abstract][Full Text] [Related]
55. Quantum mechanical quantitative structure activity relationships to avoid mutagenicity in dental monomers.
Yourtee D; Holder AJ; Smith R; Morrill JA; Kostoryz E; Brockmann W; Glaros A; Chappelow C; Eick D
J Biomater Sci Polym Ed; 2001; 12(1):89-105. PubMed ID: 11334192
[TBL] [Abstract][Full Text] [Related]
56. QSAR screening of 70,983 REACH substances for genotoxic carcinogenicity, mutagenicity and developmental toxicity in the ChemScreen project.
Wedebye EB; Dybdahl M; Nikolov NG; Jónsdóttir SÓ; Niemelä JR
Reprod Toxicol; 2015 Aug; 55():64-72. PubMed ID: 25797653
[TBL] [Abstract][Full Text] [Related]
57. QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes.
Basant N; Gupta S
Environ Sci Pollut Res Int; 2017 Jun; 24(16):14430-14444. PubMed ID: 28435990
[TBL] [Abstract][Full Text] [Related]
58. Resolution of contradiction between in silico predictions and Ames test results for four pharmaceutically relevant impurities.
Gunther WC; Kenyon MO; Cheung JR; Dugger RW; Dobo KL
Regul Toxicol Pharmacol; 2017 Dec; 91():68-76. PubMed ID: 29061373
[TBL] [Abstract][Full Text] [Related]
59. MicotoXilico: An Interactive Database to Predict Mutagenicity, Genotoxicity, and Carcinogenicity of Mycotoxins.
Tolosa J; Serrano Candelas E; Vallés Pardo JL; Goya A; Moncho S; Gozalbes R; Palomino Schätzlein M
Toxins (Basel); 2023 May; 15(6):. PubMed ID: 37368656
[TBL] [Abstract][Full Text] [Related]
60. Could deep learning in neural networks improve the QSAR models?
Gini G; Zanoli F; Gamba A; Raitano G; Benfenati E
SAR QSAR Environ Res; 2019 Sep; 30(9):617-642. PubMed ID: 31460798
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
