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 *

184 related articles for article (PubMed ID: 27564682)

  • 1. Prediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression.
    Horvath D; Marcou G; Varnek A; Kayastha S; de la Vega de León A; Bajorath J
    J Chem Inf Model; 2016 Sep; 56(9):1631-40. PubMed ID: 27564682
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Prediction of activity cliffs using support vector machines.
    Heikamp K; Hu X; Yan A; Bajorath J
    J Chem Inf Model; 2012 Sep; 52(9):2354-65. PubMed ID: 22894655
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Exploring Alternative Strategies for the Identification of Potent Compounds Using Support Vector Machine and Regression Modeling.
    Miyao T; Funatsu K; Bajorath J
    J Chem Inf Model; 2019 Mar; 59(3):983-992. PubMed ID: 30547580
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Systematic artifacts in support vector regression-based compound potency prediction revealed by statistical and activity landscape analysis.
    Balfer J; Bajorath J
    PLoS One; 2015; 10(3):e0119301. PubMed ID: 25742011
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Prediction of compound potency changes in matched molecular pairs using support vector regression.
    de la Vega de León A; Bajorath J
    J Chem Inf Model; 2014 Oct; 54(10):2654-63. PubMed ID: 25191787
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity.
    Tamura S; Miyao T; Bajorath J
    J Cheminform; 2023 Jan; 15(1):4. PubMed ID: 36611204
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Exploring QSAR models for activity-cliff prediction.
    Dablander M; Hanser T; Lambiotte R; Morris GM
    J Cheminform; 2023 Apr; 15(1):47. PubMed ID: 37069675
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Introducing a new category of activity cliffs with chemical modifications at multiple sites and rationalizing contributions of individual substitutions.
    Stumpfe D; Hu H; Bajorath J
    Bioorg Med Chem; 2019 Aug; 27(16):3605-3612. PubMed ID: 31272836
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Prediction of individual compounds forming activity cliffs using emerging chemical patterns.
    Namasivayam V; Iyer P; Bajorath J
    J Chem Inf Model; 2013 Dec; 53(12):3131-9. PubMed ID: 24304008
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Ligand-based Activity Cliff Prediction Models with Applicability Domain.
    Tamura S; Miyao T; Funatsu K
    Mol Inform; 2020 Dec; 39(12):e2000103. PubMed ID: 32830451
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Computational method for the identification of third generation activity cliffs.
    Stumpfe D; Hu H; Bajorath J
    MethodsX; 2020; 7():100793. PubMed ID: 31993342
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Systematic Exploration of Activity Cliffs Containing Privileged Substructures.
    Hu H; Bajorath J
    Mol Pharm; 2020 Mar; 17(3):979-989. PubMed ID: 31978299
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Interpretation of Ligand-Based Activity Cliff Prediction Models Using the Matched Molecular Pair Kernel.
    Tamura S; Jasial S; Miyao T; Funatsu K
    Molecules; 2021 Aug; 26(16):. PubMed ID: 34443503
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Extending the activity cliff concept: structural categorization of activity cliffs and systematic identification of different types of cliffs in the ChEMBL database.
    Hu Y; Bajorath J
    J Chem Inf Model; 2012 Jul; 52(7):1806-11. PubMed ID: 22758389
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Monitoring global growth of activity cliff information over time and assessing activity cliff frequencies and distributions.
    Stumpfe D; Bajorath J
    Future Med Chem; 2015 Aug; 7(12):1565-79. PubMed ID: 26334207
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction.
    Rodríguez-Pérez R; Vogt M; Bajorath J
    ACS Omega; 2017 Oct; 2(10):6371-6379. PubMed ID: 30023518
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Prediction of activity cliffs on the basis of images using convolutional neural networks.
    Iqbal J; Vogt M; Bajorath J
    J Comput Aided Mol Des; 2021 Dec; 35(12):1157-1164. PubMed ID: 33740200
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Data set of activity cliffs with single-atom modification and associated X-ray structure information for medicinal and computational chemistry applications.
    Hu H; Bajorath J
    Data Brief; 2020 Dec; 33():106364. PubMed ID: 33088875
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Activity cliff clusters as a source of structure-activity relationship information.
    Dimova D; Stumpfe D; Hu Y; Bajorath J
    Expert Opin Drug Discov; 2015 May; 10(5):441-7. PubMed ID: 25715967
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Do medicinal chemists learn from activity cliffs? A systematic evaluation of cliff progression in evolving compound data sets.
    Dimova D; Heikamp K; Stumpfe D; Bajorath J
    J Med Chem; 2013 Apr; 56(8):3339-45. PubMed ID: 23527828
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.