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 *

359 related articles for article (PubMed ID: 27110292)

  • 21. XLPFE: A Simple and Effective Machine Learning Scoring Function for Protein-Ligand Scoring and Ranking.
    Dong L; Qu X; Wang B
    ACS Omega; 2022 Jun; 7(25):21727-21735. PubMed ID: 35785279
    [TBL] [Abstract][Full Text] [Related]  

  • 22. 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]  

  • 23. BgN-Score and BsN-Score: bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes.
    Ashtawy HM; Mahapatra NR
    BMC Bioinformatics; 2015; 16 Suppl 4(Suppl 4):S8. PubMed ID: 25734685
    [TBL] [Abstract][Full Text] [Related]  

  • 24. 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]  

  • 25. A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening.
    Scantlebury J; Vost L; Carbery A; Hadfield TE; Turnbull OM; Brown N; Chenthamarakshan V; Das P; Grosjean H; von Delft F; Deane CM
    J Chem Inf Model; 2023 May; 63(10):2960-2974. PubMed ID: 37166179
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Does a more precise chemical description of protein-ligand complexes lead to more accurate prediction of binding affinity?
    Ballester PJ; Schreyer A; Blundell TL
    J Chem Inf Model; 2014 Mar; 54(3):944-55. PubMed ID: 24528282
    [TBL] [Abstract][Full Text] [Related]  

  • 27. The impact of cross-docked poses on performance of machine learning classifier for protein-ligand binding pose prediction.
    Shen C; Hu X; Gao J; Zhang X; Zhong H; Wang Z; Xu L; Kang Y; Cao D; Hou T
    J Cheminform; 2021 Oct; 13(1):81. PubMed ID: 34656169
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges.
    Guedes IA; Pereira FSS; Dardenne LE
    Front Pharmacol; 2018; 9():1089. PubMed ID: 30319422
    [TBL] [Abstract][Full Text] [Related]  

  • 29. MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein-Protein Docking Conformations.
    Jung Y; Geng C; Bonvin AMJJ; Xue LC; Honavar VG
    Biomolecules; 2023 Jan; 13(1):. PubMed ID: 36671507
    [TBL] [Abstract][Full Text] [Related]  

  • 30. 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]  

  • 31. Topology-Based and Conformation-Based Decoys Database: An Unbiased Online Database for Training and Benchmarking Machine-Learning Scoring Functions.
    Zhang X; Shen C; Wang T; Kang Y; Li D; Pan P; Wang J; Wang G; Deng Y; Xu L; Cao D; Hou T; Wang Z
    J Med Chem; 2023 Jul; 66(13):9174-9183. PubMed ID: 37317043
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest.
    Li H; Leung KS; Wong MH; Ballester PJ
    Molecules; 2015 Jun; 20(6):10947-62. PubMed ID: 26076113
    [TBL] [Abstract][Full Text] [Related]  

  • 33. 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]  

  • 34. Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins.
    Ashtawy HM; Mahapatra NR
    BMC Bioinformatics; 2015; 16 Suppl 6(Suppl 6):S3. PubMed ID: 25916860
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Using diverse potentials and scoring functions for the development of improved machine-learned models for protein-ligand affinity and docking pose prediction.
    Demerdash ONA
    J Comput Aided Mol Des; 2021 Nov; 35(11):1095-1123. PubMed ID: 34708263
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers.
    Gómez-Sacristán P; Simeon S; Tran-Nguyen VK; Patil S; Ballester PJ
    J Adv Res; 2024 Jan; ():. PubMed ID: 38280715
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets.
    Li H; Leung KS; Wong MH; Ballester PJ
    Mol Inform; 2015 Feb; 34(2-3):115-26. PubMed ID: 27490034
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Learning from the ligand: using ligand-based features to improve binding affinity prediction.
    Boyles F; Deane CM; Morris GM
    Bioinformatics; 2020 Feb; 36(3):758-764. PubMed ID: 31598630
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Systematic Improvement of the Performance of Machine Learning Scoring Functions by Incorporating Features of Protein-Bound Water Molecules.
    Qu X; Dong L; Zhang J; Si Y; Wang B
    J Chem Inf Model; 2022 Sep; 62(18):4369-4379. PubMed ID: 36083808
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Performance of machine-learning scoring functions in structure-based virtual screening.
    Wójcikowski M; Ballester PJ; Siedlecki P
    Sci Rep; 2017 Apr; 7():46710. PubMed ID: 28440302
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 18.