169 related articles for article (PubMed ID: 32818203)
1. Docking-based virtual screening of TβR1 inhibitors: evaluation of pose prediction and scoring functions.
Wang S; Jiang JH; Li RY; Deng P
BMC Chem; 2020 Dec; 14(1):52. PubMed ID: 32818203
[TBL] [Abstract][Full Text] [Related]
2. Impact of scoring functions on enrichment in docking-based virtual screening: an application study on renin inhibitors.
Krovat EM; Langer T
J Chem Inf Comput Sci; 2004; 44(3):1123-9. PubMed ID: 15154781
[TBL] [Abstract][Full Text] [Related]
3. Application of consensus scoring and principal component analysis for virtual screening against β-secretase (BACE-1).
Liu S; Fu R; Zhou LH; Chen SP
PLoS One; 2012; 7(6):e38086. PubMed ID: 22701601
[TBL] [Abstract][Full Text] [Related]
4. Retrospective docking study of PDE4B ligands and an analysis of the behavior of selected scoring functions.
Mpamhanga CP; Chen B; McLay IM; Ormsby DL; Lindvall MK
J Chem Inf Model; 2005; 45(4):1061-74. PubMed ID: 16045302
[TBL] [Abstract][Full Text] [Related]
5. Virtual screening to enrich a compound collection with CDK2 inhibitors using docking, scoring, and composite scoring models.
Cotesta S; Giordanetto F; Trosset JY; Crivori P; Kroemer RT; Stouten PF; Vulpetti A
Proteins; 2005 Sep; 60(4):629-43. PubMed ID: 16028223
[TBL] [Abstract][Full Text] [Related]
6. Validation studies of the site-directed docking program LibDock.
Rao SN; Head MS; Kulkarni A; LaLonde JM
J Chem Inf Model; 2007; 47(6):2159-71. PubMed ID: 17985863
[TBL] [Abstract][Full Text] [Related]
7. Relative assessment of different statistical instruments and measures for the prediction of promising outcomes using docking, virtual screening and ADMET analysis against HIV-RT.
Shamshad H; Saeed M; Ul-Haq Z; Halim SA; Gul S; Mirza AZ
J Biomol Struct Dyn; 2022 Oct; 40(17):7680-7692. PubMed ID: 33779506
[TBL] [Abstract][Full Text] [Related]
8. Structure-based virtual ligand screening with LigandFit: pose prediction and enrichment of compound collections.
Montes M; Miteva MA; Villoutreix BO
Proteins; 2007 Aug; 68(3):712-25. PubMed ID: 17510958
[TBL] [Abstract][Full Text] [Related]
9. Discovery of Novel DPP-IV Inhibitors as Potential Candidates for the Treatment of Type 2
Musoev A; Numonov S; You Z; Gao H
Molecules; 2019 Aug; 24(16):. PubMed ID: 31394858
[TBL] [Abstract][Full Text] [Related]
10. Structure-based virtual screening with supervised consensus scoring: evaluation of pose prediction and enrichment factors.
Teramoto R; Fukunishi H
J Chem Inf Model; 2008 Apr; 48(4):747-54. PubMed ID: 18318474
[TBL] [Abstract][Full Text] [Related]
11. Supervised consensus scoring for docking and virtual screening.
Teramoto R; Fukunishi H
J Chem Inf Model; 2007; 47(2):526-34. PubMed ID: 17295466
[TBL] [Abstract][Full Text] [Related]
12. SCORCH: Improving structure-based virtual screening with machine learning classifiers, data augmentation, and uncertainty estimation.
McGibbon M; Money-Kyrle S; Blay V; Houston DR
J Adv Res; 2023 Apr; 46():135-147. PubMed ID: 35901959
[TBL] [Abstract][Full Text] [Related]
13. Assessment of scoring functions and in silico parameters for AChE-ligand interactions as a tool for predicting inhibition potency.
Šinko G
Chem Biol Interact; 2019 Aug; 308():216-223. PubMed ID: 31150627
[TBL] [Abstract][Full Text] [Related]
14. A Hybrid Docking and Machine Learning Approach to Enhance the Performance of Virtual Screening Carried out on Protein-Protein Interfaces.
Singh N; Villoutreix BO
Int J Mol Sci; 2022 Nov; 23(22):. PubMed ID: 36430841
[TBL] [Abstract][Full Text] [Related]
15. Improving docking results via reranking of ensembles of ligand poses in multiple X-ray protein conformations with MM-GBSA.
Greenidge PA; Kramer C; Mozziconacci JC; Sherman W
J Chem Inf Model; 2014 Oct; 54(10):2697-717. PubMed ID: 25266271
[TBL] [Abstract][Full Text] [Related]
16. Evaluation of binary QSAR models derived from LUDI and MOE scoring functions for structure based virtual screening.
Prathipati P; Saxena AK
J Chem Inf Model; 2006; 46(1):39-51. PubMed ID: 16426038
[TBL] [Abstract][Full Text] [Related]
17. Characterization of the
Shamshad H; Hafiz A; Althagafi II; Saeed M; Mirza AZ
Curr Comput Aided Drug Des; 2020; 16(5):583-598. PubMed ID: 31453790
[TBL] [Abstract][Full Text] [Related]
18. Scoring functions and enrichment: a case study on Hsp90.
Konstantinou-Kirtay C; Mitchell JB; Lumley JA
BMC Bioinformatics; 2007 Jan; 8():27. PubMed ID: 17257425
[TBL] [Abstract][Full Text] [Related]
19. Evaluation of library ranking efficacy in virtual screening.
Kontoyianni M; Sokol GS; McClellan LM
J Comput Chem; 2005 Jan; 26(1):11-22. PubMed ID: 15526325
[TBL] [Abstract][Full Text] [Related]
20. Boosted neural networks scoring functions for accurate ligand docking and ranking.
Ashtawy HM; Mahapatra NR
J Bioinform Comput Biol; 2018 Apr; 16(2):1850004. PubMed ID: 29495922
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
