These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

173 related articles for article (PubMed ID: 26427930)

  • 1. QSRR and QSAR Studies of Antitumor Drugs in View of their Biological Activity Prediction.
    Szatkowska-Wandas P; Koba M; Smolinski G; Wandas J
    Med Chem; 2016; 12(6):592-600. PubMed ID: 26427930
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Importance of retention data from affinity and reverse-phase high-performance liquid chromatography on antitumor activity prediction of imidazoacridinones using QSAR strategy.
    Koba M; Bączek T; Marszałł MP
    J Pharm Biomed Anal; 2012 May; 64-65():87-93. PubMed ID: 22417615
    [TBL] [Abstract][Full Text] [Related]  

  • 3. The application of connected QSRR and QSAR strategies to predict the physicochemical interaction of acridinone derivatives with DNA.
    Szatkowska-Wandas P; Koba M; Kuchcicka A; Kurek S; Daghir-Wojtkowiak E; Bączek T
    Comb Chem High Throughput Screen; 2014; 17(10):820-6. PubMed ID: 25387726
    [TBL] [Abstract][Full Text] [Related]  

  • 4. QSRR prediction of the chromatographic retention behavior of painkiller drugs.
    Ghasemi J; Saaidpour S
    J Chromatogr Sci; 2009 Feb; 47(2):156-63. PubMed ID: 19222924
    [TBL] [Abstract][Full Text] [Related]  

  • 5. An application of QSRR approach and multiple linear regression method for lipophilicity assessment of flavonoids.
    Zapadka M; Kaczmarek M; Kupcewicz B; Dekowski P; Walkowiak A; Kokotkiewicz A; Łuczkiewicz M; Buciński A
    J Pharm Biomed Anal; 2019 Feb; 164():681-689. PubMed ID: 30476861
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Application of GA-MLR for QSAR Modeling of the Arylthioindole Class of Tubulin Polymerization Inhibitors as Anticancer Agents.
    Ahmadi S; Habibpour E
    Anticancer Agents Med Chem; 2017; 17(4):552-565. PubMed ID: 27528182
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Retention prediction of low molecular weight anions in ion chromatography based on quantitative structure-retention relationships applied to the linear solvent strength model.
    Park SH; Haddad PR; Talebi M; Tyteca E; Amos RI; Szucs R; Dolan JW; Pohl CA
    J Chromatogr A; 2017 Feb; 1486():68-75. PubMed ID: 28057331
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Quantitative structure-(chromatographic) retention relationship models for dissociating compounds.
    Kubik Ł; Wiczling P
    J Pharm Biomed Anal; 2016 Aug; 127():176-83. PubMed ID: 26960942
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Quantitative Structure-Activity Relationship Study of Camptothecin Derivatives as Anticancer Drugs Using Molecular Descriptors.
    Ahmadinejad N; Shafiei F
    Comb Chem High Throughput Screen; 2019; 22(6):387-399. PubMed ID: 31284856
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Lipophilicity estimation and characterization of selected steroid derivatives of biomedical importance applying RP HPLC.
    Jevrić LR; Karadžić MŽ; Mandić AI; Podunavac Kuzmanović SO; Kovačević SZ; Nikolić AR; Oklješa AM; Sakač MN; Penov Gaši KM; Stojanović SZ
    J Pharm Biomed Anal; 2017 Feb; 134():27-35. PubMed ID: 27871054
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Comparative characteristics of HPLC columns based on quantitative structure-retention relationships (QSRR) and hydrophobic-subtraction model.
    Baczek T; Kaliszan R; Novotná K; Jandera P
    J Chromatogr A; 2005 May; 1075(1-2):109-15. PubMed ID: 15974124
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Quantitative structure-retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: endogenous metabolites and banned compounds.
    Goryński K; Bojko B; Nowaczyk A; Buciński A; Pawliszyn J; Kaliszan R
    Anal Chim Acta; 2013 Oct; 797():13-9. PubMed ID: 24050665
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Molecular mechanism of retention in reversed-phase high-performance liquid chromatography and classification of modern stationary phases by using quantitative structure-retention relationships.
    Kaliszan R; van Straten MA; Markuszewski M; Cramers CA; Claessens HA
    J Chromatogr A; 1999 Sep; 855(2):455-86. PubMed ID: 10519086
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Quantitative structure-chromatographic retention relationship of synthesized peptides (HGRFG, NPNPT) and their derivatives.
    Yang X; Peng H; Han N; Zhang Z; Bai X; Zhao T; Zhao J; Liu J
    Anal Biochem; 2020 May; 597():113653. PubMed ID: 32113957
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Prediction of retention in hydrophilic interaction liquid chromatography using solute molecular descriptors based on chemical structures.
    Taraji M; Haddad PR; Amos RI; Talebi M; Szucs R; Dolan JW; Pohl CA
    J Chromatogr A; 2017 Feb; 1486():59-67. PubMed ID: 28049585
    [TBL] [Abstract][Full Text] [Related]  

  • 16. QSAR, QSPR and QSRR in Terms of 3-D-MoRSE Descriptors for In Silico Screening of Clofibric Acid Analogues.
    Di Tullio M; Maccallini C; Ammazzalorso A; Giampietro L; Amoroso R; De Filippis B; Fantacuzzi M; Wiczling P; Kaliszan R
    Mol Inform; 2012 Jul; 31(6-7):453-8. PubMed ID: 27477464
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Predictive approaches to gradient retention based on analyte structural descriptors from calculation chemistry.
    Baczek T; Kaliszan R
    J Chromatogr A; 2003 Feb; 987(1-2):29-37. PubMed ID: 12613794
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Study of chromatographic behavior of antibiotic drugs and their metabolites based on quantitative structure-retention relationships with the use of HPLC-DAD.
    Walczak-Skierska J; Szultka-Młyńska M; Pauter K; Buszewski B
    J Pharm Biomed Anal; 2020 May; 184():113187. PubMed ID: 32109708
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Quantitative structure-retention relationship of selected imidazoline derivatives on α1-acid glycoprotein column.
    Filipic S; Ruzic D; Vucicevic J; Nikolic K; Agbaba D
    J Pharm Biomed Anal; 2016 Aug; 127():101-11. PubMed ID: 26968888
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Predicting the Blood-Brain Barrier Permeability of New Drug-Like Compounds via HPLC with Various Stationary Phases.
    Janicka M; Sztanke M; Sztanke K
    Molecules; 2020 Jan; 25(3):. PubMed ID: 31979316
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.