BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

241 related articles for article (PubMed ID: 27686428)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 5. SAnDReS: A Computational Tool for Docking.
    Bitencourt-Ferreira G; de Azevedo WF
    Methods Mol Biol; 2019; 2053():51-65. PubMed ID: 31452098
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Molegro Virtual Docker for Docking.
    Bitencourt-Ferreira G; de Azevedo WF
    Methods Mol Biol; 2019; 2053():149-167. PubMed ID: 31452104
    [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. SAnDReS 2.0: Development of machine-learning models to explore the scoring function space.
    de Azevedo WF; Quiroga R; Villarreal MA; da Silveira NJF; Bitencourt-Ferreira G; da Silva AD; Veit-Acosta M; Oliveira PR; Tutone M; Biziukova N; Poroikov V; Tarasova O; Baud S
    J Comput Chem; 2024 Jun; ():. PubMed ID: 38900052
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power.
    Wang Z; Sun H; Yao X; Li D; Xu L; Li Y; Tian S; Hou T
    Phys Chem Chem Phys; 2016 May; 18(18):12964-75. PubMed ID: 27108770
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. Rescoring of docking poses under Occam's Razor: are there simpler solutions?
    Zhenin M; Bahia MS; Marcou G; Varnek A; Senderowitz H; Horvath D
    J Comput Aided Mol Des; 2018 Sep; 32(9):877-888. PubMed ID: 30173397
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Machine learning optimization of cross docking accuracy.
    Bjerrum EJ
    Comput Biol Chem; 2016 Jun; 62():133-44. PubMed ID: 27179709
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. 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]  

  • 15. The scoring bias in reverse docking and the score normalization strategy to improve success rate of target fishing.
    Luo Q; Zhao L; Hu J; Jin H; Liu Z; Zhang L
    PLoS One; 2017; 12(2):e0171433. PubMed ID: 28196116
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Task-Specific Scoring Functions for Predicting Ligand Binding Poses and Affinity and for Screening Enrichment.
    Ashtawy HM; Mahapatra NR
    J Chem Inf Model; 2018 Jan; 58(1):119-133. PubMed ID: 29190087
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets.
    Xu W; Lucke AJ; Fairlie DP
    J Mol Graph Model; 2015 Apr; 57():76-88. PubMed ID: 25682361
    [TBL] [Abstract][Full Text] [Related]  

  • 18. CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma.
    Carlson HA; Smith RD; Damm-Ganamet KL; Stuckey JA; Ahmed A; Convery MA; Somers DO; Kranz M; Elkins PA; Cui G; Peishoff CE; Lambert MH; Dunbar JB
    J Chem Inf Model; 2016 Jun; 56(6):1063-77. PubMed ID: 27149958
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Exploring the Scoring Function Space.
    Bitencourt-Ferreira G; de Azevedo WF
    Methods Mol Biol; 2019; 2053():275-281. PubMed ID: 31452111
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Target-specific native/decoy pose classifier improves the accuracy of ligand ranking in the CSAR 2013 benchmark.
    Fourches D; Politi R; Tropsha A
    J Chem Inf Model; 2015 Jan; 55(1):63-71. PubMed ID: 25521713
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.