247 related articles for article (PubMed ID: 28654262)
1. Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening.
Ericksen SS; Wu H; Zhang H; Michael LA; Newton MA; Hoffmann FM; Wildman SA
J Chem Inf Model; 2017 Jul; 57(7):1579-1590. PubMed ID: 28654262
[TBL] [Abstract][Full Text] [Related]
2. Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning.
Yasuo N; Sekijima M
J Chem Inf Model; 2019 Mar; 59(3):1050-1061. PubMed ID: 30808172
[TBL] [Abstract][Full Text] [Related]
3. MILCDock: Machine Learning Enhanced Consensus Docking for Virtual Screening in Drug Discovery.
Morris CJ; Stern JA; Stark B; Christopherson M; Della Corte D
J Chem Inf Model; 2022 Nov; 62(22):5342-5350. PubMed ID: 36342217
[TBL] [Abstract][Full Text] [Related]
4. Docking and Scoring with Target-Specific Pose Classifier Succeeds in Native-Like Pose Identification But Not Binding Affinity Prediction in the CSAR 2014 Benchmark Exercise.
Politi R; Convertino M; Popov K; Dokholyan NV; Tropsha A
J Chem Inf Model; 2016 Jun; 56(6):1032-41. PubMed ID: 27050767
[TBL] [Abstract][Full Text] [Related]
5. Protein-Ligand Empirical Interaction Components for Virtual Screening.
Yan Y; Wang W; Sun Z; Zhang JZH; Ji C
J Chem Inf Model; 2017 Aug; 57(8):1793-1806. PubMed ID: 28678484
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning.
Ricci-Lopez J; Aguila SA; Gilson MK; Brizuela CA
J Chem Inf Model; 2021 Nov; 61(11):5362-5376. PubMed ID: 34652141
[TBL] [Abstract][Full Text] [Related]
8. Optimization of compound ranking for structure-based virtual ligand screening using an established FRED-Surflex consensus approach.
Du J; Bleylevens IW; Bitorina AV; Wichapong K; Nicolaes GA
Chem Biol Drug Des; 2014 Jan; 83(1):37-51. PubMed ID: 23941463
[TBL] [Abstract][Full Text] [Related]
9. Boosting virtual screening enrichments with data fusion: coalescing hits from two-dimensional fingerprints, shape, and docking.
Sastry GM; Inakollu VS; Sherman W
J Chem Inf Model; 2013 Jul; 53(7):1531-42. PubMed ID: 23782297
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Comparison of several molecular docking programs: pose prediction and virtual screening accuracy.
Cross JB; Thompson DC; Rai BK; Baber JC; Fan KY; Hu Y; Humblet C
J Chem Inf Model; 2009 Jun; 49(6):1455-74. PubMed ID: 19476350
[TBL] [Abstract][Full Text] [Related]
12. The Development of Target-Specific Pose Filter Ensembles To Boost Ligand Enrichment for Structure-Based Virtual Screening.
Xia J; Hsieh JH; Hu H; Wu S; Wang XS
J Chem Inf Model; 2017 Jun; 57(6):1414-1425. PubMed ID: 28511009
[TBL] [Abstract][Full Text] [Related]
13. Evaluation of consensus scoring methods for AutoDock Vina, smina and idock.
Masters L; Eagon S; Heying M
J Mol Graph Model; 2020 May; 96():107532. PubMed ID: 31991303
[TBL] [Abstract][Full Text] [Related]
14. Using consensus-shape clustering to identify promiscuous ligands and protein targets and to choose the right query for shape-based virtual screening.
Pérez-Nueno VI; Ritchie DW
J Chem Inf Model; 2011 Jun; 51(6):1233-48. PubMed ID: 21604699
[TBL] [Abstract][Full Text] [Related]
15. Improving Docking-Based Virtual Screening Ability by Integrating Multiple Energy Auxiliary Terms from Molecular Docking Scoring.
Ye WL; Shen C; Xiong GL; Ding JJ; Lu AP; Hou TJ; Cao DS
J Chem Inf Model; 2020 Sep; 60(9):4216-4230. PubMed ID: 32352294
[TBL] [Abstract][Full Text] [Related]
16. Computational protein-ligand docking and virtual drug screening with the AutoDock suite.
Forli S; Huey R; Pique ME; Sanner MF; Goodsell DS; Olson AJ
Nat Protoc; 2016 May; 11(5):905-19. PubMed ID: 27077332
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Application of Shape Similarity in Pose Selection and Virtual Screening in CSARdock2014 Exercise.
Kumar A; Zhang KY
J Chem Inf Model; 2016 Jun; 56(6):965-73. PubMed ID: 26247231
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Ranking targets in structure-based virtual screening of three-dimensional protein libraries: methods and problems.
Kellenberger E; Foata N; Rognan D
J Chem Inf Model; 2008 May; 48(5):1014-25. PubMed ID: 18412328
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
