171 related articles for article (PubMed ID: 31130601)
1. Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds.
Mora Lagares L; Minovski N; Novič M
Molecules; 2019 May; 24(10):. PubMed ID: 31130601
[TBL] [Abstract][Full Text] [Related]
2. ADMET evaluation in drug discovery. 13. Development of in silico prediction models for P-glycoprotein substrates.
Li D; Chen L; Li Y; Tian S; Sun H; Hou T
Mol Pharm; 2014 Mar; 11(3):716-26. PubMed ID: 24499501
[TBL] [Abstract][Full Text] [Related]
3. Computational models for identifying potential P-glycoprotein substrates and inhibitors.
Crivori P; Reinach B; Pezzetta D; Poggesi I
Mol Pharm; 2006; 3(1):33-44. PubMed ID: 16686367
[TBL] [Abstract][Full Text] [Related]
4. Development of in Silico Models for Predicting P-Glycoprotein Inhibitors Based on a Two-Step Approach for Feature Selection and Its Application to Chinese Herbal Medicine Screening.
Yang M; Chen J; Shi X; Xu L; Xi Z; You L; An R; Wang X
Mol Pharm; 2015 Oct; 12(10):3691-713. PubMed ID: 26376206
[TBL] [Abstract][Full Text] [Related]
5. Predicting P-glycoprotein-mediated drug transport based on support vector machine and three-dimensional crystal structure of P-glycoprotein.
Bikadi Z; Hazai I; Malik D; Jemnitz K; Veres Z; Hari P; Ni Z; Loo TW; Clarke DM; Hazai E; Mao Q
PLoS One; 2011; 6(10):e25815. PubMed ID: 21991360
[TBL] [Abstract][Full Text] [Related]
6. Development of Simplified in Vitro P-Glycoprotein Substrate Assay and in Silico Prediction Models To Evaluate Transport Potential of P-Glycoprotein.
Ohashi R; Watanabe R; Esaki T; Taniguchi T; Torimoto-Katori N; Watanabe T; Ogasawara Y; Takahashi T; Tsukimoto M; Mizuguchi K
Mol Pharm; 2019 May; 16(5):1851-1863. PubMed ID: 30933526
[TBL] [Abstract][Full Text] [Related]
7. Prediction of promiscuous p-glycoprotein inhibition using a novel machine learning scheme.
Leong MK; Chen HB; Shih YH
PLoS One; 2012; 7(3):e33829. PubMed ID: 22439003
[TBL] [Abstract][Full Text] [Related]
8. Integration of in silico and in vitro tools for scaffold optimization during drug discovery: predicting P-glycoprotein efflux.
Desai PV; Sawada GA; Watson IA; Raub TJ
Mol Pharm; 2013 Apr; 10(4):1249-61. PubMed ID: 23363443
[TBL] [Abstract][Full Text] [Related]
9. Fingerprint-based in silico models for the prediction of P-glycoprotein substrates and inhibitors.
Poongavanam V; Haider N; Ecker GF
Bioorg Med Chem; 2012 Sep; 20(18):5388-95. PubMed ID: 22595422
[TBL] [Abstract][Full Text] [Related]
10. A Machine Learning-Based Prediction Platform for P-Glycoprotein Modulators and Its Validation by Molecular Docking.
Kadioglu O; Efferth T
Cells; 2019 Oct; 8(10):. PubMed ID: 31640190
[TBL] [Abstract][Full Text] [Related]
11. Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates.
Esposito C; Wang S; Lange UEW; Oellien F; Riniker S
J Chem Inf Model; 2020 Oct; 60(10):4730-4749. PubMed ID: 32786699
[TBL] [Abstract][Full Text] [Related]
12. A computational ensemble pharmacophore model for identifying substrates of P-glycoprotein.
Penzotti JE; Lamb ML; Evensen E; Grootenhuis PD
J Med Chem; 2002 Apr; 45(9):1737-40. PubMed ID: 11960484
[TBL] [Abstract][Full Text] [Related]
13. Image-based QSAR Model for the Prediction of P-gp Inhibitory Activity of Epigallocatechin and Gallocatechin Derivatives.
Ghaemian P; Shayanfar A
Curr Comput Aided Drug Des; 2019; 15(3):212-224. PubMed ID: 30280673
[TBL] [Abstract][Full Text] [Related]
14. Ligand and structure-based classification models for prediction of P-glycoprotein inhibitors.
Klepsch F; Vasanthanathan P; Ecker GF
J Chem Inf Model; 2014 Jan; 54(1):218-29. PubMed ID: 24050383
[TBL] [Abstract][Full Text] [Related]
15. Theoretical Prediction of the Complex P-Glycoprotein Substrate Efflux Based on the Novel Hierarchical Support Vector Regression Scheme.
Chen C; Lee MH; Weng CF; Leong MK
Molecules; 2018 Jul; 23(7):. PubMed ID: 30037151
[TBL] [Abstract][Full Text] [Related]
16. Role of transmembrane domain/transmembrane domain interfaces of P-glycoprotein (ABCB1) in solute transport. Convergent information from photoaffinity labeling, site directed mutagenesis and in silico importance prediction.
Chiba P; Mihalek I; Ecker GF; Kopp S; Lichtarge O
Curr Med Chem; 2006; 13(7):793-805. PubMed ID: 16611068
[TBL] [Abstract][Full Text] [Related]
17. Development of an in silico model for predicting efflux substrates in Caco-2 cells.
Zhang L; Balimane PV; Johnson SR; Chong S
Int J Pharm; 2007 Oct; 343(1-2):98-105. PubMed ID: 17583455
[TBL] [Abstract][Full Text] [Related]
18. Validation and application of Caco-2 assays for the in vitro evaluation of development candidate drugs as substrates or inhibitors of P-glycoprotein to support regulatory submissions.
Elsby R; Surry DD; Smith VN; Gray AJ
Xenobiotica; 2008 Jul; 38(7-8):1140-64. PubMed ID: 18668443
[TBL] [Abstract][Full Text] [Related]
19. Pharmacokinetic and pharmacodynamic implications of P-glycoprotein modulation.
Matheny CJ; Lamb MW; Brouwer KR; Pollack GM
Pharmacotherapy; 2001 Jul; 21(7):778-96. PubMed ID: 11444575
[TBL] [Abstract][Full Text] [Related]
20. Emerging significance of flavonoids as P-glycoprotein inhibitors in cancer chemotherapy.
Bansal T; Jaggi M; Khar RK; Talegaonkar S
J Pharm Pharm Sci; 2009; 12(1):46-78. PubMed ID: 19470292
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
