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 *

186 related articles for article (PubMed ID: 28641555)

  • 1. Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity.
    Heck GS; Pintro VO; Pereira RR; de Ávila MB; Levin NMB; de Azevedo WF
    Curr Med Chem; 2017; 24(23):2459-2470. PubMed ID: 28641555
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2.
    Bitencourt-Ferreira G; Duarte da Silva A; Filgueira de Azevedo W
    Curr Med Chem; 2021; 28(2):253-265. PubMed ID: 31729287
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Machine Learning to Predict Binding Affinity.
    Bitencourt-Ferreira G; de Azevedo WF
    Methods Mol Biol; 2019; 2053():251-273. PubMed ID: 31452110
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS.
    Bitencourt-Ferreira G; Rizzotto C; de Azevedo Junior WF
    Curr Med Chem; 2021; 28(9):1746-1756. PubMed ID: 32410551
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2.
    de Ávila MB; Xavier MM; Pintro VO; de Azevedo WF
    Biochem Biophys Res Commun; 2017 Dec; 494(1-2):305-310. PubMed ID: 29017921
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes.
    Bitencourt-Ferreira G; de Azevedo WF
    Biophys Chem; 2018 Sep; 240():63-69. PubMed ID: 29906639
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Machine learning in computational docking.
    Khamis MA; Gomaa W; Ahmed WF
    Artif Intell Med; 2015 Mar; 63(3):135-52. PubMed ID: 25724101
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery.
    Veit-Acosta M; de Azevedo Junior WF
    Curr Med Chem; 2022; 29(14):2438-2455. PubMed ID: 34365938
    [TBL] [Abstract][Full Text] [Related]  

  • 9. The Impact of Crystallographic Data for the Development of Machine Learning Models to Predict Protein-Ligand Binding Affinity.
    Veit-Acosta M; de Azevedo Junior WF
    Curr Med Chem; 2021 Oct; 28(34):7006-7022. PubMed ID: 33568025
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Electrostatic Potential Energy in Protein-Drug Complexes.
    Bitencourt-Ferreira G; de Azevedo Junior WF
    Curr Med Chem; 2021; 28(24):4954-4971. PubMed ID: 33593246
    [TBL] [Abstract][Full Text] [Related]  

  • 11. SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions.
    Xavier MM; Heck GS; Avila MB; Levin NMB; Pintro VO; Carvalho NL; Azevedo WF
    Comb Chem High Throughput Screen; 2016; 19(10):801-812. PubMed ID: 27686428
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Development of CDK-targeted scoring functions for prediction of binding affinity.
    Levin NMB; Pintro VO; Bitencourt-Ferreira G; de Mattos BB; de Castro Silvério A; de Azevedo WF
    Biophys Chem; 2018 Apr; 235():1-8. PubMed ID: 29407904
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Taba: A Tool to Analyze the Binding Affinity.
    da Silva AD; Bitencourt-Ferreira G; de Azevedo WF
    J Comput Chem; 2020 Jan; 41(1):69-73. PubMed ID: 31410856
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Optimized Virtual Screening Workflow: Towards Target-Based Polynomial Scoring Functions for HIV-1 Protease.
    Pintro VO; de Azevedo WF
    Comb Chem High Throughput Screen; 2017; 20(9):820-827. PubMed ID: 29165067
    [TBL] [Abstract][Full Text] [Related]  

  • 15. CScore: a simple yet effective scoring function for protein-ligand binding affinity prediction using modified CMAC learning architecture.
    Ouyang X; Handoko SD; Kwoh CK
    J Bioinform Comput Biol; 2011 Dec; 9 Suppl 1():1-14. PubMed ID: 22144250
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Machine Learning Meets Physics-based Modeling: A Mass-spring System to Predict Protein-ligand Binding Affinity.
    Filgueira de Azevedo W
    Curr Med Chem; 2024 Aug; ():. PubMed ID: 39092736
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Beware of machine learning-based scoring functions-on the danger of developing black boxes.
    Gabel J; Desaphy J; Rognan D
    J Chem Inf Model; 2014 Oct; 54(10):2807-15. PubMed ID: 25207678
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Use of machine learning approaches for novel drug discovery.
    Lima AN; Philot EA; Trossini GH; Scott LP; Maltarollo VG; Honorio KM
    Expert Opin Drug Discov; 2016; 11(3):225-39. PubMed ID: 26814169
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Empirical Scoring Functions for Affinity Prediction of Protein-ligand Complexes.
    Pason LP; Sotriffer CA
    Mol Inform; 2016 Dec; 35(11-12):541-548. PubMed ID: 27870243
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Computationally predicting binding affinity in protein-ligand complexes: free energy-based simulations and machine learning-based scoring functions.
    Wang DD; Zhu M; Yan H
    Brief Bioinform; 2021 May; 22(3):. PubMed ID: 32591817
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.