194 related articles for article (PubMed ID: 30023710)
1. Superior Performance of the SQM/COSMO Scoring Functions in Native Pose Recognition of Diverse Protein-Ligand Complexes in Cognate Docking.
Ajani H; Pecina A; Eyrilmez SM; Fanfrlík J; Haldar S; Řezáč J; Hobza P; Lepšík M
ACS Omega; 2017 Jul; 2(7):4022-4029. PubMed ID: 30023710
[TBL] [Abstract][Full Text] [Related]
2. SQM/COSMO Scoring Function at the DFTB3-D3H4 Level: Unique Identification of Native Protein-Ligand Poses.
Pecina A; Haldar S; Fanfrlík J; Meier R; Řezáč J; Lepšík M; Hobza P
J Chem Inf Model; 2017 Feb; 57(2):127-132. PubMed ID: 28045518
[TBL] [Abstract][Full Text] [Related]
3. The SQM/COSMO filter: reliable native pose identification based on the quantum-mechanical description of protein-ligand interactions and implicit COSMO solvation.
Pecina A; Meier R; Fanfrlík J; Lepšík M; Řezáč J; Hobza P; Baldauf C
Chem Commun (Camb); 2016 Feb; 52(16):3312-5. PubMed ID: 26821703
[TBL] [Abstract][Full Text] [Related]
4. SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design.
Pecina A; Eyrilmez SM; Köprülüoğlu C; Miriyala VM; Lepšík M; Fanfrlík J; Řezáč J; Hobza P
Chempluschem; 2020 Nov; 85(11):2362-2371. PubMed ID: 32609421
[TBL] [Abstract][Full Text] [Related]
5. Impressive Enrichment of Semiempirical Quantum Mechanics-Based Scoring Function: HSP90 Protein with 4541 Inhibitors and Decoys.
Eyrilmez SM; Köprülüoğlu C; Řezáč J; Hobza P
Chemphyschem; 2019 Nov; 20(21):2759-2766. PubMed ID: 31460692
[TBL] [Abstract][Full Text] [Related]
6. Ranking Power of the SQM/COSMO Scoring Function on Carbonic Anhydrase II-Inhibitor Complexes.
Pecina A; Brynda J; Vrzal L; Gnanasekaran R; Hořejší M; Eyrilmez SM; Řezáč J; Lepšík M; Řezáčová P; Hobza P; Majer P; Veverka V; Fanfrlík J
Chemphyschem; 2018 Apr; 19(7):873-879. PubMed ID: 29316128
[TBL] [Abstract][Full Text] [Related]
7. Quantum-Chemical Quasi-Docking for Molecular Dynamics Calculations.
Sulimov A; Kutov D; Ilin I; Sulimov V
Nanomaterials (Basel); 2022 Jan; 12(2):. PubMed ID: 35055291
[TBL] [Abstract][Full Text] [Related]
8. Improving protein-ligand docking results using the Semiempirical quantum mechanics: testing on the PDBbind 2016 core set.
Mohebbinia Z; Firouzi R; Karimi-Jafari MH
J Biomol Struct Dyn; 2024 Jan; ():1-11. PubMed ID: 38165642
[TBL] [Abstract][Full Text] [Related]
9. Using the Semiempirical Quantum Mechanics in Improving the Molecular Docking: A Case Study with CDK2.
Bagheri S; Behnejad H; Firouzi R; Karimi-Jafari MH
Mol Inform; 2020 Sep; 39(9):e2000036. PubMed ID: 32485047
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Calculations on noncovalent interactions and databases of benchmark interaction energies.
Hobza P
Acc Chem Res; 2012 Apr; 45(4):663-72. PubMed ID: 22225511
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Macrocycle Conformational Sampling by DFT-D3/COSMO-RS Methodology.
Gutten O; Bím D; Řezáč J; Rulíšek L
J Chem Inf Model; 2018 Jan; 58(1):48-60. PubMed ID: 29182321
[TBL] [Abstract][Full Text] [Related]
14. SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design.
Pecina A; Eyrilmez SM; Köprülüoğlu C; Miriyala VM; Lepšík M; Fanfrlík J; Řezáč J; Hobza P
Chempluschem; 2020 Nov; 85(11):2361. PubMed ID: 32986310
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. 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]
18. 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]
19. AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility.
Ravindranath PA; Forli S; Goodsell DS; Olson AJ; Sanner MF
PLoS Comput Biol; 2015 Dec; 11(12):e1004586. PubMed ID: 26629955
[TBL] [Abstract][Full Text] [Related]
20. The Performance of Several Docking Programs at Reproducing Protein-Macrolide-Like Crystal Structures.
Castro-Alvarez A; Costa AM; Vilarrasa J
Molecules; 2017 Jan; 22(1):. PubMed ID: 28106755
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
