153 related articles for article (PubMed ID: 27748625)
21. Deep learning methods for protein torsion angle prediction.
Li H; Hou J; Adhikari B; Lyu Q; Cheng J
BMC Bioinformatics; 2017 Sep; 18(1):417. PubMed ID: 28923002
[TBL] [Abstract][Full Text] [Related]
22. Scoring docked conformations generated by rigid-body protein-protein docking.
Camacho CJ; Gatchell DW; Kimura SR; Vajda S
Proteins; 2000 Aug; 40(3):525-37. PubMed ID: 10861944
[TBL] [Abstract][Full Text] [Related]
23. A combinatorial scoring function for protein-RNA docking.
Zhang Z; Lu L; Zhang Y; Hua Li C; Wang CX; Zhang XY; Tan JJ
Proteins; 2017 Apr; 85(4):741-752. PubMed ID: 28120375
[TBL] [Abstract][Full Text] [Related]
24. A soft docking algorithm for predicting the structures of protein-protein complexes.
Li CH; Ma XH; Chen WZ; Wang CX
Sheng Wu Hua Xue Yu Sheng Wu Wu Li Xue Bao (Shanghai); 2003 Jan; 35(1):35-40. PubMed ID: 12518225
[TBL] [Abstract][Full Text] [Related]
25. The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction.
Li H; Peng J; Leung Y; Leung KS; Wong MH; Lu G; Ballester PJ
Biomolecules; 2018 Mar; 8(1):. PubMed ID: 29538331
[TBL] [Abstract][Full Text] [Related]
26. H-bond network optimization in protein-protein complexes: are all-atom force field scores enough?
Masone D; Vaca IC; Pons C; Recio JF; Guallar V
Proteins; 2012 Mar; 80(3):818-24. PubMed ID: 22113891
[TBL] [Abstract][Full Text] [Related]
27. Scoring a diverse set of high-quality docked conformations: a metascore based on electrostatic and desolvation interactions.
Camacho CJ; Ma H; Champ PC
Proteins; 2006 Jun; 63(4):868-77. PubMed ID: 16506242
[TBL] [Abstract][Full Text] [Related]
28. Ab initio prediction of peptide-MHC binding geometry for diverse class I MHC allotypes.
Bordner AJ; Abagyan R
Proteins; 2006 May; 63(3):512-26. PubMed ID: 16470819
[TBL] [Abstract][Full Text] [Related]
29. Efficient identification of near-native conformations in ab initio protein structure prediction using structural profiles.
Wolff K; Vendruscolo M; Porto M
Proteins; 2010 Feb; 78(2):249-58. PubMed ID: 19701942
[TBL] [Abstract][Full Text] [Related]
30. Empirical Scoring Functions for Affinity Prediction of Protein-ligand Complexes.
Pason LP; Sotriffer CA
Mol Inform; 2016 Dec; 35(11-12):541-548. PubMed ID: 27870243
[TBL] [Abstract][Full Text] [Related]
31. An evolutionary conservation-based method for refining and reranking protein complex structures.
Akbal-Delibas B; Hashmi I; Shehu A; Haspel N
J Bioinform Comput Biol; 2012 Jun; 10(3):1242002. PubMed ID: 22809378
[TBL] [Abstract][Full Text] [Related]
32. Structure-based prediction of transcription factor binding sites using a protein-DNA docking approach.
Liu Z; Guo JT; Li T; Xu Y
Proteins; 2008 Sep; 72(4):1114-24. PubMed ID: 18320590
[TBL] [Abstract][Full Text] [Related]
33. Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes.
Bitencourt-Ferreira G; de Azevedo WF
Biophys Chem; 2018 Sep; 240():63-69. PubMed ID: 29906639
[TBL] [Abstract][Full Text] [Related]
34. Rapid Design of Knowledge-Based Scoring Potentials for Enrichment of Near-Native Geometries in Protein-Protein Docking.
Sasse A; de Vries SJ; Schindler CE; de BeauchĂȘne IC; Zacharias M
PLoS One; 2017; 12(1):e0170625. PubMed ID: 28118389
[TBL] [Abstract][Full Text] [Related]
35. Machine learning optimization of cross docking accuracy.
Bjerrum EJ
Comput Biol Chem; 2016 Jun; 62():133-44. PubMed ID: 27179709
[TBL] [Abstract][Full Text] [Related]
36. 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]
37. Complementarity of hydrophobic properties in ATP-protein binding: a new criterion to rank docking solutions.
Pyrkov TV; Kosinsky YA; Arseniev AS; Priestle JP; Jacoby E; Efremov RG
Proteins; 2007 Feb; 66(2):388-98. PubMed ID: 17094116
[TBL] [Abstract][Full Text] [Related]
38. Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization.
Kadukova M; Grudinin S
J Comput Aided Mol Des; 2017 Oct; 31(10):943-958. PubMed ID: 28921375
[TBL] [Abstract][Full Text] [Related]
39. Rapid activity prediction of HIV-1 integrase inhibitors: harnessing docking energetic components for empirical scoring by chemometric and artificial neural network approaches.
Thangsunan P; Kittiwachana S; Meepowpan P; Kungwan N; Prangkio P; Hannongbua S; Suree N
J Comput Aided Mol Des; 2016 Jun; 30(6):471-88. PubMed ID: 27314501
[TBL] [Abstract][Full Text] [Related]
40. ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance.
Fan N; Bauer CA; Stork C; de Bruyn Kops C; Kirchmair J
Mol Inform; 2020 Apr; 39(4):e1900103. PubMed ID: 31663691
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]