152 related articles for article (PubMed ID: 23541745)
41. New QSPR study for the prediction of aqueous solubility of drug-like compounds.
Duchowicz PR; Talevi A; Bruno-Blanch LE; Castro EA
Bioorg Med Chem; 2008 Sep; 16(17):7944-55. PubMed ID: 18701302
[TBL] [Abstract][Full Text] [Related]
42. Testing computational toxicology models with phytochemicals.
Valerio LG; Arvidson KB; Busta E; Minnier BL; Kruhlak NL; Benz RD
Mol Nutr Food Res; 2010 Feb; 54(2):186-94. PubMed ID: 20024931
[TBL] [Abstract][Full Text] [Related]
43. A strategy for risk management of drug-induced phospholipidosis.
Chatman LA; Morton D; Johnson TO; Anway SD
Toxicol Pathol; 2009 Dec; 37(7):997-1005. PubMed ID: 20008549
[TBL] [Abstract][Full Text] [Related]
44. In silico categorization of in vivo intrinsic clearance using machine learning.
Hsiao YW; Fagerholm U; Norinder U
Mol Pharm; 2013 Apr; 10(4):1318-21. PubMed ID: 23427914
[TBL] [Abstract][Full Text] [Related]
45. QSAR-based permeability model for drug-like compounds.
Gozalbes R; Jacewicz M; Annand R; Tsaioun K; Pineda-Lucena A
Bioorg Med Chem; 2011 Apr; 19(8):2615-24. PubMed ID: 21458999
[TBL] [Abstract][Full Text] [Related]
46. Development and validation of in silico models for estimating drug preformulation risk in PEG400/water and Tween80/water systems.
Crivori P; Morelli A; Pezzetta D; Rocchetti M; Poggesi I
Eur J Pharm Sci; 2007 Nov; 32(3):169-81. PubMed ID: 17714921
[TBL] [Abstract][Full Text] [Related]
47. In silico screening of chemicals for bacterial mutagenicity using electrotopological E-state indices and MDL QSAR software.
Contrera JF; Matthews EJ; Kruhlak NL; Benz RD
Regul Toxicol Pharmacol; 2005 Dec; 43(3):313-23. PubMed ID: 16242226
[TBL] [Abstract][Full Text] [Related]
48. A 96-well flow cytometric screening assay for detecting in vitro phospholipidosis-induction in the drug discovery phase.
Natalie M; Margino S; Erik H; Annelieke P; Geert V; Philippe V
Toxicol In Vitro; 2009 Mar; 23(2):217-26. PubMed ID: 19101623
[TBL] [Abstract][Full Text] [Related]
49. Virtual high-throughput screening of molecular databases.
Seifert MH; Kraus J; Kramer B
Curr Opin Drug Discov Devel; 2007 May; 10(3):298-307. PubMed ID: 17554856
[TBL] [Abstract][Full Text] [Related]
50. 2D MI-DRAGON: a new predictor for protein-ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins.
Prado-Prado F; García-Mera X; Escobar M; Sobarzo-Sánchez E; Yañez M; Riera-Fernandez P; González-Díaz H
Eur J Med Chem; 2011 Dec; 46(12):5838-51. PubMed ID: 22005185
[TBL] [Abstract][Full Text] [Related]
51. Predicting myelosuppression of drugs from in silico models.
Crivori P; Pennella G; Magistrelli M; Grossi P; Giusti AM
J Chem Inf Model; 2011 Feb; 51(2):434-45. PubMed ID: 21275392
[TBL] [Abstract][Full Text] [Related]
52. Phospholipidosis effect of drugs by adsorption into lipid monolayers.
Ceccarelli M; Germani R; Massari S; Petit C; Nurisso A; Wolfender JL; Goracci L
Colloids Surf B Biointerfaces; 2015 Dec; 136():175-84. PubMed ID: 26387069
[TBL] [Abstract][Full Text] [Related]
53. Genetic toxicity assessment: employing the best science for human safety evaluation. Part I: Early screening for potential human mutagens.
Jacobson-Kram D; Contrera JF
Toxicol Sci; 2007 Mar; 96(1):16-20. PubMed ID: 17194803
[TBL] [Abstract][Full Text] [Related]
54. Modeling phospholipidosis induction: reliability and warnings.
Goracci L; Ceccarelli M; Bonelli D; Cruciani G
J Chem Inf Model; 2013 Jun; 53(6):1436-46. PubMed ID: 23692521
[TBL] [Abstract][Full Text] [Related]
55. In silico methods for early toxicity assessment.
Merlot C
Curr Opin Drug Discov Devel; 2008 Jan; 11(1):80-5. PubMed ID: 18175270
[TBL] [Abstract][Full Text] [Related]
56. 3D-MEDNEs: an alternative "in silico" technique for chemical research in toxicology. 2. quantitative proteome-toxicity relationships (QPTR) based on mass spectrum spiral entropy.
Cruz-Monteagudo M; González-Díaz H; Borges F; Dominguez ER; Cordeiro MN
Chem Res Toxicol; 2008 Mar; 21(3):619-32. PubMed ID: 18257557
[TBL] [Abstract][Full Text] [Related]
57. Prediction of antibacterial compounds by machine learning approaches.
Yang XG; Chen D; Wang M; Xue Y; Chen YZ
J Comput Chem; 2009 Jun; 30(8):1202-11. PubMed ID: 18988254
[TBL] [Abstract][Full Text] [Related]
58. Hologram QSAR model for the prediction of human oral bioavailability.
Moda TL; Montanari CA; Andricopulo AD
Bioorg Med Chem; 2007 Dec; 15(24):7738-45. PubMed ID: 17870541
[TBL] [Abstract][Full Text] [Related]
59. Exploiting ensemble learning to improve prediction of phospholipidosis inducing potential.
Nath A; Sahu GK
J Theor Biol; 2019 Oct; 479():37-47. PubMed ID: 31310757
[TBL] [Abstract][Full Text] [Related]
60. The value of in silico chemistry in the safety assessment of chemicals in the consumer goods and pharmaceutical industries.
Modi S; Hughes M; Garrow A; White A
Drug Discov Today; 2012 Feb; 17(3-4):135-42. PubMed ID: 22063083
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
