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 *

161 related articles for article (PubMed ID: 17346118)

  • 1. Target specific compound identification using a support vector machine.
    Plewczynski D; von Grotthuss M; Spieser SA; Rychlewski L; Wyrwicz LS; Ginalski K; Koch U
    Comb Chem High Throughput Screen; 2007 Mar; 10(3):189-96. PubMed ID: 17346118
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Evaluation of virtual screening performance of support vector machines trained by sparsely distributed active compounds.
    Ma XH; Wang R; Yang SY; Li ZR; Xue Y; Wei YC; Low BC; Chen YZ
    J Chem Inf Model; 2008 Jun; 48(6):1227-37. PubMed ID: 18533644
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Virtual high throughput screening using combined random forest and flexible docking.
    Plewczynski D; von Grotthuss M; Rychlewski L; Ginalski K
    Comb Chem High Throughput Screen; 2009 Jun; 12(5):484-9. PubMed ID: 19519327
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Structure-based drug screening and ligand-based drug screening with machine learning.
    Fukunishi Y
    Comb Chem High Throughput Screen; 2009 May; 12(4):397-408. PubMed ID: 19442067
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Ligand prediction from protein sequence and small molecule information using support vector machines and fingerprint descriptors.
    Geppert H; Humrich J; Stumpfe D; Gärtner T; Bajorath J
    J Chem Inf Model; 2009 Apr; 49(4):767-79. PubMed ID: 19309114
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Virtual screening of Abl inhibitors from large compound libraries by support vector machines.
    Liu XH; Ma XH; Tan CY; Jiang YY; Go ML; Low BC; Chen YZ
    J Chem Inf Model; 2009 Sep; 49(9):2101-10. PubMed ID: 19689138
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.
    Fang X; Bagui S; Bagui S
    Comput Biol Chem; 2017 Aug; 69():110-119. PubMed ID: 28601761
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods.
    Zang Q; Rotroff DM; Judson RS
    J Chem Inf Model; 2013 Dec; 53(12):3244-61. PubMed ID: 24279462
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Development of CYP3A4 inhibition models: comparisons of machine-learning techniques and molecular descriptors.
    Arimoto R; Prasad MA; Gifford EM
    J Biomol Screen; 2005 Apr; 10(3):197-205. PubMed ID: 15809315
    [TBL] [Abstract][Full Text] [Related]  

  • 10. An efficient in silico screening method based on the protein-compound affinity matrix and its application to the design of a focused library for cytochrome P450 (CYP) ligands.
    Fukunishi Y; Hojo S; Nakamura H
    J Chem Inf Model; 2006; 46(6):2610-22. PubMed ID: 17125201
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery.
    Fang J; Yang R; Gao L; Zhou D; Yang S; Liu AL; Du GH
    J Chem Inf Model; 2013 Nov; 53(11):3009-20. PubMed ID: 24144102
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Using machine learning methods to predict experimental high-throughput screening data.
    Mballo C; Makarenkov V
    Comb Chem High Throughput Screen; 2010 Jun; 13(5):430-41. PubMed ID: 20236062
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Multistage virtual screening and identification of novel HIV-1 protease inhibitors by integrating SVM, shape, pharmacophore and docking methods.
    Wei Y; Li J; Chen Z; Wang F; Huang W; Hong Z; Lin J
    Eur J Med Chem; 2015 Aug; 101():409-18. PubMed ID: 26185005
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Protein-Ligand Empirical Interaction Components for Virtual Screening.
    Yan Y; Wang W; Sun Z; Zhang JZH; Ji C
    J Chem Inf Model; 2017 Aug; 57(8):1793-1806. PubMed ID: 28678484
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Potency-directed similarity searching using support vector machines.
    Wassermann AM; Heikamp K; Bajorath J
    Chem Biol Drug Des; 2011 Jan; 77(1):30-8. PubMed ID: 21114788
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Combining machine learning and pharmacophore-based interaction fingerprint for in silico screening.
    Sato T; Honma T; Yokoyama S
    J Chem Inf Model; 2010 Jan; 50(1):170-85. PubMed ID: 20038188
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor.
    Han LY; Ma XH; Lin HH; Jia J; Zhu F; Xue Y; Li ZR; Cao ZW; Ji ZL; Chen YZ
    J Mol Graph Model; 2008 Jun; 26(8):1276-86. PubMed ID: 18218332
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Computational Prediction of Compound-Protein Interactions for Orphan Targets Using CGBVS.
    Kanai C; Kawasaki E; Murakami R; Morita Y; Yoshimori A
    Molecules; 2021 Aug; 26(17):. PubMed ID: 34500569
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. kNNsim: k-nearest neighbors similarity with genetic algorithm features optimization enhances the prediction of activity classes for small molecules.
    Plewczynski D
    J Mol Model; 2009 Jun; 15(6):591-6. PubMed ID: 18663491
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.