195 related articles for article (PubMed ID: 22101402)
1. DemQSAR: predicting human volume of distribution and clearance of drugs.
Demir-Kavuk O; Bentzien J; Muegge I; Knapp EW
J Comput Aided Mol Des; 2011 Dec; 25(12):1121-33. PubMed ID: 22101402
[TBL] [Abstract][Full Text] [Related]
2. Predicting Volume of Distribution in Humans: Performance of In Silico Methods for a Large Set of Structurally Diverse Clinical Compounds.
Murad N; Pasikanti KK; Madej BD; Minnich A; McComas JM; Crouch S; Polli JW; Weber AD
Drug Metab Dispos; 2021 Feb; 49(2):169-178. PubMed ID: 33239335
[TBL] [Abstract][Full Text] [Related]
3. Intravitreal clearance and volume of distribution of compounds in rabbits: In silico prediction and pharmacokinetic simulations for drug development.
del Amo EM; Vellonen KS; Kidron H; Urtti A
Eur J Pharm Biopharm; 2015 Sep; 95(Pt B):215-26. PubMed ID: 25603198
[TBL] [Abstract][Full Text] [Related]
4. Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay.
Kosugi Y; Hosea N
Mol Pharm; 2020 Jul; 17(7):2299-2309. PubMed ID: 32478525
[TBL] [Abstract][Full Text] [Related]
5. Prediction of steady-state volume of distribution of acidic drugs by quantitative structure-pharmacokinetics relationships.
Zhivkova Z; Doytchinova I
J Pharm Sci; 2012 Mar; 101(3):1253-66. PubMed ID: 22170307
[TBL] [Abstract][Full Text] [Related]
6. Current approaches for choosing feature selection and learning algorithms in quantitative structure-activity relationships (QSAR).
Khan PM; Roy K
Expert Opin Drug Discov; 2018 Dec; 13(12):1075-1089. PubMed ID: 30372648
[TBL] [Abstract][Full Text] [Related]
7. Prediction of human pharmacokinetics from animal data and molecular structural parameters using multivariate regression analysis: volume of distribution at steady state.
Wajima T; Fukumura K; Yano Y; Oguma T
J Pharm Pharmacol; 2003 Jul; 55(7):939-49. PubMed ID: 12906751
[TBL] [Abstract][Full Text] [Related]
8. Predicting blood-to-plasma concentration ratios of drugs from chemical structures and volumes of distribution in humans.
Mamada H; Iwamoto K; Nomura Y; Uesawa Y
Mol Divers; 2021 Aug; 25(3):1261-1270. PubMed ID: 33569705
[TBL] [Abstract][Full Text] [Related]
9. Quantitative structure-activity relationship models of clinical pharmacokinetics: clearance and volume of distribution.
Gombar VK; Hall SD
J Chem Inf Model; 2013 Apr; 53(4):948-57. PubMed ID: 23451981
[TBL] [Abstract][Full Text] [Related]
10. Quantitative structure-retention-pharmacokinetic relationship studies.
Agatonovic-Kustrin S; Turner JV; Glass BD
Drug Metab Lett; 2008 Apr; 2(2):130-7. PubMed ID: 19356082
[TBL] [Abstract][Full Text] [Related]
11. Quantitative structure-pharmacokinetic relationship modelling: apparent volume of distribution.
Ghafourian T; Barzegar-Jalali M; Hakimiha N; Cronin MT
J Pharm Pharmacol; 2004 Mar; 56(3):339-50. PubMed ID: 15025859
[TBL] [Abstract][Full Text] [Related]
12. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 3: comparative assessement of prediction methods of human clearance.
Ring BJ; Chien JY; Adkison KK; Jones HM; Rowland M; Jones RD; Yates JW; Ku MS; Gibson CR; He H; Vuppugalla R; Marathe P; Fischer V; Dutta S; Sinha VK; Björnsson T; Lavé T; Poulin P
J Pharm Sci; 2011 Oct; 100(10):4090-110. PubMed ID: 21541938
[TBL] [Abstract][Full Text] [Related]
13. Predictive QSAR modeling for the successful predictions of the ADMET properties of candidate drug molecules.
Khan MT; Sylte I
Curr Drug Discov Technol; 2007 Oct; 4(3):141-9. PubMed ID: 17985997
[TBL] [Abstract][Full Text] [Related]
14. QSPR models for the prediction of apparent volume of distribution.
Ghafourian T; Barzegar-Jalali M; Dastmalchi S; Khavari-Khorasani T; Hakimiha N; Nokhodchi A
Int J Pharm; 2006 Aug; 319(1-2):82-97. PubMed ID: 16698204
[TBL] [Abstract][Full Text] [Related]
15. Three-dimensional quantitative structure activity relationship computational approaches for prediction of human in vitro intrinsic clearance.
Ekins S; Obach RS
J Pharmacol Exp Ther; 2000 Nov; 295(2):463-73. PubMed ID: 11046077
[TBL] [Abstract][Full Text] [Related]
16. In Silico Prediction of Compounds Binding to Human Plasma Proteins by QSAR Models.
Sun L; Yang H; Li J; Wang T; Li W; Liu G; Tang Y
ChemMedChem; 2018 Mar; 13(6):572-581. PubMed ID: 29057587
[TBL] [Abstract][Full Text] [Related]
17. Gaussian processes: a method for automatic QSAR modeling of ADME properties.
Obrezanova O; Csanyi G; Gola JM; Segall MD
J Chem Inf Model; 2007; 47(5):1847-57. PubMed ID: 17602549
[TBL] [Abstract][Full Text] [Related]
18. Predicting drug metabolism and pharmacokinetics features of in-house compounds by a hybrid machine-learning model.
Sasahara K; Shibata M; Sasabe H; Suzuki T; Takeuchi K; Umehara K; Kashiyama E
Drug Metab Pharmacokinet; 2021 Aug; 39():100395. PubMed ID: 33991751
[TBL] [Abstract][Full Text] [Related]
19. First-principle, structure-based prediction of hepatic metabolic clearance values in human.
Li H; Sun J; Sui X; Liu J; Yan Z; Liu X; Sun Y; He Z
Eur J Med Chem; 2009 Apr; 44(4):1600-6. PubMed ID: 18768239
[TBL] [Abstract][Full Text] [Related]
20. Recent developments of in silico predictions of intestinal absorption and oral bioavailability.
Hou T; Li Y; Zhang W; Wang J
Comb Chem High Throughput Screen; 2009 Jun; 12(5):497-506. PubMed ID: 19519329
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]