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 *

230 related articles for article (PubMed ID: 30130102)

  • 1. Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening.
    Cortés-Ciriano I; Firth NC; Bender A; Watson O
    J Chem Inf Model; 2018 Sep; 58(9):2000-2014. PubMed ID: 30130102
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Evaluation of QSAR Equations for Virtual Screening.
    Spiegel J; Senderowitz H
    Int J Mol Sci; 2020 Oct; 21(21):. PubMed ID: 33105703
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Discovery of multitarget-directed ligands against Alzheimer's disease through systematic prediction of chemical-protein interactions.
    Fang J; Li Y; Liu R; Pang X; Li C; Yang R; He Y; Lian W; Liu AL; Du GH
    J Chem Inf Model; 2015 Jan; 55(1):149-64. PubMed ID: 25531792
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Comparing the Influence of Simulated Experimental Errors on 12 Machine Learning Algorithms in Bioactivity Modeling Using 12 Diverse Data Sets.
    Cortes-Ciriano I; Bender A; Malliavin TE
    J Chem Inf Model; 2015 Jul; 55(7):1413-25. PubMed ID: 26038978
    [TBL] [Abstract][Full Text] [Related]  

  • 5. How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space.
    Koutsoukas A; Paricharak S; Galloway WR; Spring DR; Ijzerman AP; Glen RC; Marcus D; Bender A
    J Chem Inf Model; 2014 Jan; 54(1):230-42. PubMed ID: 24289493
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Similarity searching for potent compounds using feature selection.
    Vogt M; Bajorath J
    J Chem Inf Model; 2013 Jul; 53(7):1613-9. PubMed ID: 23808911
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Data-Driven Derivation of an "Informer Compound Set" for Improved Selection of Active Compounds in High-Throughput Screening.
    Paricharak S; IJzerman AP; Jenkins JL; Bender A; Nigsch F
    J Chem Inf Model; 2016 Sep; 56(9):1622-30. PubMed ID: 27487177
    [TBL] [Abstract][Full Text] [Related]  

  • 8. WDL-RF: predicting bioactivities of ligand molecules acting with G protein-coupled receptors by combining weighted deep learning and random forest.
    Wu J; Zhang Q; Wu W; Pang T; Hu H; Chan WKB; Ke X; Zhang Y
    Bioinformatics; 2018 Jul; 34(13):2271-2282. PubMed ID: 29432522
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Critical comparison of virtual screening methods against the MUV data set.
    Tiikkainen P; Markt P; Wolber G; Kirchmair J; Distinto S; Poso A; Kallioniemi O
    J Chem Inf Model; 2009 Oct; 49(10):2168-78. PubMed ID: 19799417
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A decision-theoretic approach to the evaluation of machine learning algorithms in computational drug discovery.
    Watson OP; Cortes-Ciriano I; Taylor AR; Watson JA
    Bioinformatics; 2019 Nov; 35(22):4656-4663. PubMed ID: 31070704
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Experimental design strategy: weak reinforcement leads to increased hit rates and enhanced chemical diversity.
    Maciejewski M; Wassermann AM; Glick M; Lounkine E
    J Chem Inf Model; 2015 May; 55(5):956-62. PubMed ID: 25915687
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Practical Model Selection for Prospective Virtual Screening.
    Liu S; Alnammi M; Ericksen SS; Voter AF; Ananiev GE; Keck JL; Hoffmann FM; Wildman SA; Gitter A
    J Chem Inf Model; 2019 Jan; 59(1):282-293. PubMed ID: 30500183
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach.
    Egieyeh S; Syce J; Malan SF; Christoffels A
    PLoS One; 2018; 13(9):e0204644. PubMed ID: 30265702
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Accurate Hit Estimation for Iterative Screening Using Venn-ABERS Predictors.
    Buendia R; Kogej T; Engkvist O; Carlsson L; Linusson H; Johansson U; Toccaceli P; Ahlberg E
    J Chem Inf Model; 2019 Mar; 59(3):1230-1237. PubMed ID: 30726080
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Molecular Similarity-Based Domain Applicability Metric Efficiently Identifies Out-of-Domain Compounds.
    Liu R; Wallqvist A
    J Chem Inf Model; 2019 Jan; 59(1):181-189. PubMed ID: 30404432
    [TBL] [Abstract][Full Text] [Related]  

  • 16. MOST: most-similar ligand based approach to target prediction.
    Huang T; Mi H; Lin CY; Zhao L; Zhong LL; Liu FB; Zhang G; Lu AP; Bian ZX;
    BMC Bioinformatics; 2017 Mar; 18(1):165. PubMed ID: 28284192
    [TBL] [Abstract][Full Text] [Related]  

  • 17. All-Assay-Max2 pQSAR: Activity Predictions as Accurate as Four-Concentration IC
    Martin EJ; Polyakov VR; Zhu XW; Tian L; Mukherjee P; Liu X
    J Chem Inf Model; 2019 Oct; 59(10):4450-4459. PubMed ID: 31518124
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Changing the HTS Paradigm: AI-Driven Iterative Screening for Hit Finding.
    Dreiman GHS; Bictash M; Fish PV; Griffin L; Svensson F
    SLAS Discov; 2021 Feb; 26(2):257-262. PubMed ID: 32808550
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Comparison of methods for sequential screening of large compound sets.
    Blower PE; Cross KP; Eichler GS; Myatt GJ; Weinstein JN; Yang C
    Comb Chem High Throughput Screen; 2006 Feb; 9(2):115-22. PubMed ID: 16475969
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Predicting kinase inhibitors using bioactivity matrix derived informer sets.
    Zhang H; Ericksen SS; Lee CP; Ananiev GE; Wlodarchak N; Yu P; Mitchell JC; Gitter A; Wright SJ; Hoffmann FM; Wildman SA; Newton MA
    PLoS Comput Biol; 2019 Aug; 15(8):e1006813. PubMed ID: 31381559
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 12.