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 *

124 related articles for article (PubMed ID: 37943257)

  • 1. Anatomy of Potency Predictions Focusing on Structural Analogues with Increasing Potency Differences Including Activity Cliffs.
    Janela T; Bajorath J
    J Chem Inf Model; 2023 Nov; 63(22):7032-7044. PubMed ID: 37943257
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 5. Rationalizing general limitations in assessing and comparing methods for compound potency prediction.
    Janela T; Bajorath J
    Sci Rep; 2023 Oct; 13(1):17816. PubMed ID: 37857835
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Prediction of Promiscuity Cliffs Using Machine Learning.
    Blaschke T; Feldmann C; Bajorath J
    Mol Inform; 2021 Jan; 40(1):e2000196. PubMed ID: 32881355
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Searching for coordinated activity cliffs using particle swarm optimization.
    Namasivayam V; Bajorath J
    J Chem Inf Model; 2012 Apr; 52(4):927-34. PubMed ID: 22404190
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Generation of Molecular Counterfactuals for Explainable Machine Learning Based on Core-Substituent Recombination.
    Lamens A; Bajorath J
    ChemMedChem; 2024 Feb; 19(3):e202300586. PubMed ID: 37983655
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 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. 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]  

  • 12. 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]  

  • 13. MMP-Cliffs: systematic identification of activity cliffs on the basis of matched molecular pairs.
    Hu X; Hu Y; Vogt M; Stumpfe D; Bajorath J
    J Chem Inf Model; 2012 May; 52(5):1138-45. PubMed ID: 22489665
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. 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]  

  • 16. Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions.
    Rodríguez-Pérez R; Bajorath J
    J Comput Aided Mol Des; 2020 Oct; 34(10):1013-1026. PubMed ID: 32361862
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Exposing the Limitations of Molecular Machine Learning with Activity Cliffs.
    van Tilborg D; Alenicheva A; Grisoni F
    J Chem Inf Model; 2022 Dec; 62(23):5938-5951. PubMed ID: 36456532
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Large-Scale Predictions of Compound Potency with Original and Modified Activity Classes Reveal General Prediction Characteristics and Intrinsic Limitations of Conventional Benchmarking Calculations.
    Janela T; Bajorath J
    Pharmaceuticals (Basel); 2023 Apr; 16(4):. PubMed ID: 37111287
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Explaining Multiclass Compound Activity Predictions Using Counterfactuals and Shapley Values.
    Lamens A; Bajorath J
    Molecules; 2023 Jul; 28(14):. PubMed ID: 37513472
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.