307 related articles for article (PubMed ID: 34656169)
1. 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]
2. 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]
3. Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained on Docked Poses.
Boyles F; Deane CM; Morris GM
J Chem Inf Model; 2022 Nov; 62(22):5329-5341. PubMed ID: 34469150
[TBL] [Abstract][Full Text] [Related]
4. Correcting the impact of docking pose generation error on binding affinity prediction.
Li H; Leung KS; Wong MH; Ballester PJ
BMC Bioinformatics; 2016 Sep; 17(Suppl 11):308. PubMed ID: 28185549
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. RNAPosers: Machine Learning Classifiers for Ribonucleic Acid-Ligand Poses.
Chhabra S; Xie J; Frank AT
J Phys Chem B; 2020 Jun; 124(22):4436-4445. PubMed ID: 32427491
[TBL] [Abstract][Full Text] [Related]
7. Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?
Shen C; Weng G; Zhang X; Leung EL; Yao X; Pang J; Chai X; Li D; Wang E; Cao D; Hou T
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33418562
[TBL] [Abstract][Full Text] [Related]
8. A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function.
Wang Z; Zheng L; Wang S; Lin M; Wang Z; Kong AW; Mu Y; Wei Y; Li W
Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36502369
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. 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]
12. Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design.
Francoeur PG; Masuda T; Sunseri J; Jia A; Iovanisci RB; Snyder I; Koes DR
J Chem Inf Model; 2020 Sep; 60(9):4200-4215. PubMed ID: 32865404
[TBL] [Abstract][Full Text] [Related]
13. Extended connectivity interaction features: improving binding affinity prediction through chemical description.
Sánchez-Cruz N; Medina-Franco JL; Mestres J; Barril X
Bioinformatics; 2021 Jun; 37(10):1376-1382. PubMed ID: 33226061
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. 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]
16. RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method.
Tan LH; Kwoh CK; Mu Y
Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38695120
[TBL] [Abstract][Full Text] [Related]
17. Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation.
Grudinin S; Kadukova M; Eisenbarth A; Marillet S; Cazals F
J Comput Aided Mol Des; 2016 Sep; 30(9):791-804. PubMed ID: 27718029
[TBL] [Abstract][Full Text] [Related]
18. Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power.
Wang Z; Sun H; Yao X; Li D; Xu L; Li Y; Tian S; Hou T
Phys Chem Chem Phys; 2016 May; 18(18):12964-75. PubMed ID: 27108770
[TBL] [Abstract][Full Text] [Related]
19. Open-ComBind: harnessing unlabeled data for improved binding pose prediction.
McNutt AT; Koes DR
J Comput Aided Mol Des; 2023 Dec; 38(1):3. PubMed ID: 38062207
[TBL] [Abstract][Full Text] [Related]
20. DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction.
Velec HF; Gohlke H; Klebe G
J Med Chem; 2005 Oct; 48(20):6296-303. PubMed ID: 16190756
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
