166 related articles for article (PubMed ID: 37606194)
1. zPoseScore model for accurate and robust protein-ligand docking pose scoring in CASP15.
Shen T; Liu F; Wang Z; Sun J; Bu Y; Meng J; Chen W; Yao K; Mu Y; Li W; Zhao G; Wang S; Wei Y; Zheng L
Proteins; 2023 Dec; 91(12):1837-1849. PubMed ID: 37606194
[TBL] [Abstract][Full Text] [Related]
2. CoDock-Ligand: combined template-based docking and CNN-based scoring in ligand binding prediction.
Pang M; He W; Lu X; She Y; Xie L; Kong R; Chang S
BMC Bioinformatics; 2023 Nov; 24(1):444. PubMed ID: 37996806
[TBL] [Abstract][Full Text] [Related]
3. Assessment of protein-ligand complexes in CASP15.
Robin X; Studer G; Durairaj J; Eberhardt J; Schwede T; Walters WP
Proteins; 2023 Dec; 91(12):1811-1821. PubMed ID: 37795762
[TBL] [Abstract][Full Text] [Related]
4. Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results.
Li Y; Han L; Liu Z; Wang R
J Chem Inf Model; 2014 Jun; 54(6):1717-36. PubMed ID: 24708446
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Automated benchmarking of combined protein structure and ligand conformation prediction.
Leemann M; Sagasta A; Eberhardt J; Schwede T; Robin X; Durairaj J
Proteins; 2023 Dec; 91(12):1912-1924. PubMed ID: 37885318
[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. 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]
9. ViTScore: A Novel Three-Dimensional Vision Transformer Method for Accurate Prediction of Protein-Ligand Docking Poses.
Guo L; Qiu T; Wang J
IEEE Trans Nanobioscience; 2023 Oct; 22(4):734-743. PubMed ID: 37159314
[TBL] [Abstract][Full Text] [Related]
10. Forging the Basis for Developing Protein-Ligand Interaction Scoring Functions.
Liu Z; Su M; Han L; Liu J; Yang Q; Li Y; Wang R
Acc Chem Res; 2017 Feb; 50(2):302-309. PubMed ID: 28182403
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Template-guided method for protein-ligand complex structure prediction: Application to CASP15 protein-ligand studies.
Xu X; Duan R; Zou X
Proteins; 2023 Dec; 91(12):1829-1836. PubMed ID: 37283068
[TBL] [Abstract][Full Text] [Related]
13. DeepBSP-a Machine Learning Method for Accurate Prediction of Protein-Ligand Docking Structures.
Bao J; He X; Zhang JZH
J Chem Inf Model; 2021 May; 61(5):2231-2240. PubMed ID: 33979150
[TBL] [Abstract][Full Text] [Related]
14. Deep Scoring Neural Network Replacing the Scoring Function Components to Improve the Performance of Structure-Based Molecular Docking.
Yang L; Yang G; Chen X; Yang Q; Yao X; Bing Z; Niu Y; Huang L; Yang L
ACS Chem Neurosci; 2021 Jun; 12(12):2133-2142. PubMed ID: 34081851
[TBL] [Abstract][Full Text] [Related]
15. Statistical potential for modeling and ranking of protein-ligand interactions.
Fan H; Schneidman-Duhovny D; Irwin JJ; Dong G; Shoichet BK; Sali A
J Chem Inf Model; 2011 Dec; 51(12):3078-92. PubMed ID: 22014038
[TBL] [Abstract][Full Text] [Related]
16. Accurate ligand-protein docking in CASP15 using the ClusPro LigTBM server.
Kotelnikov S; Ashizawa R; Popov KI; Khan O; Ignatov M; Li SX; Hassan M; Coutsias EA; Poda G; Padhorny D; Tropsha A; Vajda S; Kozakov D
Proteins; 2023 Dec; 91(12):1822-1828. PubMed ID: 37697630
[TBL] [Abstract][Full Text] [Related]
17. Geometry Optimization Algorithms in Conjunction with the Machine Learning Potential ANI-2x Facilitate the Structure-Based Virtual Screening and Binding Mode Prediction.
Wang L; He X; Ji B; Han F; Niu T; Cai L; Zhai J; Hao D; Wang J
Biomolecules; 2024 May; 14(6):. PubMed ID: 38927052
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. DOX: A new computational protocol for accurate prediction of the protein-ligand binding structures.
Rao L; Chi B; Ren Y; Li Y; Xu X; Wan J
J Comput Chem; 2016 Jan; 37(3):336-44. PubMed ID: 26459237
[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]