332 related articles for article (PubMed ID: 36430841)
1. 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]
2. Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein-Protein Interfaces.
Singh N; Chaput L; Villoutreix BO
J Chem Inf Model; 2020 Aug; 60(8):3910-3934. PubMed ID: 32786511
[TBL] [Abstract][Full Text] [Related]
3. Assessment of a novel scoring method based on solvent accessible surface area descriptors.
Núñez S; Venhorst J; Kruse CG
J Chem Inf Model; 2010 Apr; 50(4):480-6. PubMed ID: 20356089
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Efficient conformational sampling and weak scoring in docking programs? Strategy of the wisdom of crowds.
Chaput L; Mouawad L
J Cheminform; 2017 Jun; 9(1):37. PubMed ID: 29086077
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. 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]
9. Improved protein-ligand docking using GOLD.
Verdonk ML; Cole JC; Hartshorn MJ; Murray CW; Taylor RD
Proteins; 2003 Sep; 52(4):609-23. PubMed ID: 12910460
[TBL] [Abstract][Full Text] [Related]
10. Towards an Enrichment Optimization Algorithm (EOA)-based Target Specific Docking Functions for Virtual Screening.
Spiegel J; Senderowitz H
Mol Inform; 2022 Nov; 41(11):e2200034. PubMed ID: 35790469
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set?
Su M; Feng G; Liu Z; Li Y; Wang R
J Chem Inf Model; 2020 Mar; 60(3):1122-1136. PubMed ID: 32085675
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Cheminformatics meets molecular mechanics: a combined application of knowledge-based pose scoring and physical force field-based hit scoring functions improves the accuracy of structure-based virtual screening.
Hsieh JH; Yin S; Wang XS; Liu S; Dokholyan NV; Tropsha A
J Chem Inf Model; 2012 Jan; 52(1):16-28. PubMed ID: 22017385
[TBL] [Abstract][Full Text] [Related]
15. Benchmarking docking and scoring protocol for the identification of potential acetylcholinesterase inhibitors.
Zaheer-ul-Haq ; Halim SA; Uddin R; Madura JD
J Mol Graph Model; 2010 Jun; 28(8):870-82. PubMed ID: 20447848
[TBL] [Abstract][Full Text] [Related]
16. Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest.
Wang C; Zhang Y
J Comput Chem; 2017 Jan; 38(3):169-177. PubMed ID: 27859414
[TBL] [Abstract][Full Text] [Related]
17. Structure-based virtual screening of the nociceptin receptor: hybrid docking and shape-based approaches for improved hit identification.
Daga PR; Polgar WE; Zaveri NT
J Chem Inf Model; 2014 Oct; 54(10):2732-43. PubMed ID: 25148595
[TBL] [Abstract][Full Text] [Related]
18. A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening.
Spiegel J; Senderowitz H
Int J Mol Sci; 2021 Dec; 23(1):. PubMed ID: 35008467
[TBL] [Abstract][Full Text] [Related]
19. PharmRF: A machine-learning scoring function to identify the best protein-ligand complexes for structure-based pharmacophore screening with high enrichments.
Kumar SP; Dixit NY; Patel CN; Rawal RM; Pandya HA
J Comput Chem; 2022 May; 43(12):847-863. PubMed ID: 35301752
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
