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 *

199 related articles for article (PubMed ID: 33517778)

  • 1. SAR and QSAR research on tyrosinase inhibitors using machine learning methods.
    Wu Y; Huo D; Chen G; Yan A
    SAR QSAR Environ Res; 2021 Feb; 32(2):85-110. PubMed ID: 33517778
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Classification and QSAR models of leukotriene A4 hydrolase (LTA4H) inhibitors by machine learning methods.
    Qin R; Wang H; Yan A
    SAR QSAR Environ Res; 2021 May; 32(5):411-431. PubMed ID: 33896285
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Predicting tyrosinase inhibition by 3D QSAR pharmacophore models and designing potential tyrosinase inhibitors from Traditional Chinese medicine database.
    Gao H
    Phytomedicine; 2018 Jan; 38():145-157. PubMed ID: 29425647
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Classification of FLT3 inhibitors and SAR analysis by machine learning methods.
    Zhao Y; Tian Y; Pang X; Li G; Shi S; Yan A
    Mol Divers; 2024 Aug; 28(4):1995-2011. PubMed ID: 37142889
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A SAR and QSAR study on 3CLpro inhibitors of SARS-CoV-2 using machine learning methods.
    Zhang Y; Tian Y; Yan A
    SAR QSAR Environ Res; 2024 Jul; 35(7):531-563. PubMed ID: 39077983
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Quantitative structure-activity relationship studies of mushroom tyrosinase inhibitors.
    Xue CB; Luo WC; Ding Q; Liu SZ; Gao XX
    J Comput Aided Mol Des; 2008 May; 22(5):299-309. PubMed ID: 18256890
    [TBL] [Abstract][Full Text] [Related]  

  • 7. SAR study on inhibitors of GIIA secreted phospholipase A
    Zhang S; Li Y; Qin Z; Tu G; Chen G; Yan A
    Chem Biol Drug Des; 2019 May; 93(5):666-684. PubMed ID: 30582300
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Prediction of tyrosinase inhibition activity using atom-based bilinear indices.
    Marrero-Ponce Y; Khan MT; Casañola Martín GM; Ather A; Sultankhodzhaev MN; Torrens F; Rotondo R
    ChemMedChem; 2007 Apr; 2(4):449-78. PubMed ID: 17366651
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Predictive models for tyrosinase inhibitors: Challenges from heterogeneous activity data determined by different experimental protocols.
    Tang H; Cui F; Liu L; Li Y
    Comput Biol Chem; 2018 Apr; 73():79-84. PubMed ID: 29471263
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Classification models and SAR analysis on thromboxane A
    Ji Y; Li R; Tian Y; Chen G; Yan A
    SAR QSAR Environ Res; 2022 Jun; 33(6):429-462. PubMed ID: 35678125
    [TBL] [Abstract][Full Text] [Related]  

  • 11. TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors: evaluation of different classification model combinations using bond-based linear indices.
    Casañola-Martín GM; Marrero-Ponce Y; Khan MT; Ather A; Sultan S; Torrens F; Rotondo R
    Bioorg Med Chem; 2007 Feb; 15(3):1483-503. PubMed ID: 17110117
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Bond-based 2D quadratic fingerprints in QSAR studies: virtual and in vitro tyrosinase inhibitory activity elucidation.
    Casañola-Martin GM; Marrero-Ponce Y; Khan MT; Khan SB; Torrens F; Pérez-Jiménez F; Rescigno A; Abad C
    Chem Biol Drug Des; 2010 Dec; 76(6):538-45. PubMed ID: 20964806
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Prediction of bioactivities of microsomal prostaglandin E
    Tian Y; Yang Z; Wang H; Yan A
    Chem Biol Drug Des; 2023 Jun; 101(6):1307-1321. PubMed ID: 36752697
    [TBL] [Abstract][Full Text] [Related]  

  • 14. SAR and QSAR models of cyclooxygenase-1 (COX-1) inhibitors.
    Xi Y; Qin Z; Yan A
    SAR QSAR Environ Res; 2018 Oct; 29(10):755-784. PubMed ID: 30274533
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Novel coumarin-based tyrosinase inhibitors discovered by OECD principles-validated QSAR approach from an enlarged, balanced database.
    Le-Thi-Thu H; Casañola-Martín GM; Marrero-Ponce Y; Rescigno A; Saso L; Parmar VS; Torrens F; Abad C
    Mol Divers; 2011 May; 15(2):507-20. PubMed ID: 20814821
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Construction of QSAR model based on cysteine-containing dipeptides and screening of natural tyrosinase inhibitors.
    Li X; Pan F; Yang Z; Gao F; Li J; Zhang F; Wang T
    J Food Biochem; 2022 Oct; 46(10):e14338. PubMed ID: 35933724
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Classification models and SAR analysis on HDAC1 inhibitors using machine learning methods.
    Li R; Tian Y; Yang Z; Ji Y; Ding J; Yan A
    Mol Divers; 2023 Jun; 27(3):1037-1051. PubMed ID: 35737257
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Vanilloid derivatives as tyrosinase inhibitors driven by virtual screening-based QSAR models.
    Rescigno A; Casañola-Martin GM; Sanjust E; Zucca P; Marrero-Ponce Y
    Drug Test Anal; 2011 Mar; 3(3):176-81. PubMed ID: 21125547
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Development of predictive QSAR models for the substrates/inhibitors of OATP1B1 by deep neural networks.
    Gui C; Li Y; Peng T
    Toxicol Lett; 2023 Mar; 376():20-25. PubMed ID: 36649904
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Computational strategies towards developing novel antimelanogenic agents.
    Unni PA; Lulu SS; Pillai GG
    Life Sci; 2020 Jun; 250():117602. PubMed ID: 32240677
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.