284 related articles for article (PubMed ID: 32153730)
1. Predicting the impacts of mutations on protein-ligand binding affinity based on molecular dynamics simulations and machine learning methods.
Wang DD; Ou-Yang L; Xie H; Zhu M; Yan H
Comput Struct Biotechnol J; 2020; 18():439-454. PubMed ID: 32153730
[TBL] [Abstract][Full Text] [Related]
2. A New Hybrid Neural Network Deep Learning Method for Protein-Ligand Binding Affinity Prediction and De Novo Drug Design.
Limbu S; Dakshanamurthy S
Int J Mol Sci; 2022 Nov; 23(22):. PubMed ID: 36430386
[TBL] [Abstract][Full Text] [Related]
3. Time-Domain Analysis of Molecular Dynamics Trajectories Using Deep Neural Networks: Application to Activity Ranking of Tankyrase Inhibitors.
Berishvili VP; Perkin VO; Voronkov AE; Radchenko EV; Syed R; Venkata Ramana Reddy C; Pillay V; Kumar P; Choonara YE; Kamal A; Palyulin VA
J Chem Inf Model; 2019 Aug; 59(8):3519-3532. PubMed ID: 31276400
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Can molecular dynamics simulations improve predictions of protein-ligand binding affinity with machine learning?
Gu S; Shen C; Yu J; Zhao H; Liu H; Liu L; Sheng R; Xu L; Wang Z; Hou T; Kang Y
Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36681903
[TBL] [Abstract][Full Text] [Related]
6. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations.
Hou T; Wang J; Li Y; Wang W
J Chem Inf Model; 2011 Jan; 51(1):69-82. PubMed ID: 21117705
[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. Assessing the performance of the MM/PBSA and MM/GBSA methods. 10. Impacts of enhanced sampling and variable dielectric model on protein-protein Interactions.
Wang E; Weng G; Sun H; Du H; Zhu F; Chen F; Wang Z; Hou T
Phys Chem Chem Phys; 2019 Sep; 21(35):18958-18969. PubMed ID: 31453590
[TBL] [Abstract][Full Text] [Related]
9. Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions.
Seo S; Choi J; Park S; Ahn J
BMC Bioinformatics; 2021 Nov; 22(1):542. PubMed ID: 34749664
[TBL] [Abstract][Full Text] [Related]
10. Assessing the performance of MM/PBSA and MM/GBSA methods. 3. The impact of force fields and ligand charge models.
Xu L; Sun H; Li Y; Wang J; Hou T
J Phys Chem B; 2013 Jul; 117(28):8408-21. PubMed ID: 23789789
[TBL] [Abstract][Full Text] [Related]
11. Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations.
Agajanian S; Oluyemi O; Verkhivker GM
Front Mol Biosci; 2019; 6():44. PubMed ID: 31245384
[TBL] [Abstract][Full Text] [Related]
12. Molecular dynamics-solvated interaction energy studies of protein-protein interactions: the MP1-p14 scaffolding complex.
Cui Q; Sulea T; Schrag JD; Munger C; Hung MN; Naïm M; Cygler M; Purisima EO
J Mol Biol; 2008 Jun; 379(4):787-802. PubMed ID: 18479705
[TBL] [Abstract][Full Text] [Related]
13. Decoding of finger trajectory from ECoG using deep learning.
Xie Z; Schwartz O; Prasad A
J Neural Eng; 2018 Jun; 15(3):036009. PubMed ID: 29182152
[TBL] [Abstract][Full Text] [Related]
14. WDL-RF: predicting bioactivities of ligand molecules acting with G protein-coupled receptors by combining weighted deep learning and random forest.
Wu J; Zhang Q; Wu W; Pang T; Hu H; Chan WKB; Ke X; Zhang Y
Bioinformatics; 2018 Jul; 34(13):2271-2282. PubMed ID: 29432522
[TBL] [Abstract][Full Text] [Related]
15. Machine Learning From Molecular Dynamics Trajectories to Predict Caspase-8 Inhibitors Against Alzheimer's Disease.
Jamal S; Grover A; Grover S
Front Pharmacol; 2019; 10():780. PubMed ID: 31354494
[TBL] [Abstract][Full Text] [Related]
16. Develop and test a solvent accessible surface area-based model in conformational entropy calculations.
Wang J; Hou T
J Chem Inf Model; 2012 May; 52(5):1199-212. PubMed ID: 22497310
[TBL] [Abstract][Full Text] [Related]
17. SMPLIP-Score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors.
Kumar S; Kim MH
J Cheminform; 2021 Mar; 13(1):28. PubMed ID: 33766140
[TBL] [Abstract][Full Text] [Related]
18. Neural networks prediction of the protein-ligand binding affinity with circular fingerprints.
Yin Z; Song W; Li B; Wang F; Xie L; Xu X
Technol Health Care; 2023; 31(S1):487-495. PubMed ID: 37066944
[TBL] [Abstract][Full Text] [Related]
19. PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity.
Ammar A; Cavill R; Evelo C; Willighagen E
J Cheminform; 2023 Mar; 15(1):31. PubMed ID: 36864534
[TBL] [Abstract][Full Text] [Related]
20. Fibroblast growth factor 5 (FGF5) and its missense mutant FGF5-H174 underlying trichomegaly: a molecular dynamics simulation investigation.
Hoang SH
J Biomol Struct Dyn; 2023; 41(24):14786-14796. PubMed ID: 36905676
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]