512 related articles for article (PubMed ID: 33034255)
1. Ligand-based pharmacophore modeling of TNF-α to design novel inhibitors using virtual screening and molecular dynamics.
Jade DD; Pandey R; Kumar R; Gupta D
J Biomol Struct Dyn; 2022 Mar; 40(4):1702-1718. PubMed ID: 33034255
[TBL] [Abstract][Full Text] [Related]
2. Novel tumor necrosis factor-α (TNF-α) inhibitors from small molecule library screening for their therapeutic activity profiles against rheumatoid arthritis using target-driven approaches and binary QSAR models.
Zaka M; Abbasi BH; Durdagi S
J Biomol Struct Dyn; 2019 Jun; 37(9):2464-2476. PubMed ID: 30047845
[TBL] [Abstract][Full Text] [Related]
3. Structure-Based Virtual Screening of Tumor Necrosis Factor-α Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation.
Halim SA; Sikandari AG; Khan A; Wadood A; Fatmi MQ; Csuk R; Al-Harrasi A
Biomolecules; 2021 Feb; 11(2):. PubMed ID: 33671607
[TBL] [Abstract][Full Text] [Related]
4. Structural insight into TNF-α inhibitors through combining pharmacophore-based virtual screening and molecular dynamic simulation.
Qaiser H; Saeed M; Nerukh D; Ul-Haq Z
J Biomol Struct Dyn; 2021 Oct; 39(16):5920-5939. PubMed ID: 32705954
[TBL] [Abstract][Full Text] [Related]
5. Proposing novel TNFα direct inhibitor Scaffolds using fragment-docking based e-pharmacophore modeling and binary QSAR-based virtual screening protocols pipeline.
Zaka M; Abbasi BH; Durdagi S
J Mol Graph Model; 2018 Oct; 85():111-121. PubMed ID: 30149308
[TBL] [Abstract][Full Text] [Related]
6. Pharmacophore modeling, multiple docking, and molecular dynamics studies on Wee1 kinase inhibitors.
Hu Y; Zhou L; Zhu X; Dai D; Bao Y; Qiu Y
J Biomol Struct Dyn; 2019 Jul; 37(10):2703-2715. PubMed ID: 30052133
[TBL] [Abstract][Full Text] [Related]
7. Discovery of potential inhibitors for stat3: ligand based 3D pharmacophore, virtual screening, molecular docking, dynamic studies and
Lakshmanan K; T K P; K Pai SR; Rajagopal K; Byran G
J Biomol Struct Dyn; 2022; 40(21):11320-11338. PubMed ID: 34463213
[TBL] [Abstract][Full Text] [Related]
8. Pharmacophore-based virtual screening of ZINC database, molecular modeling and designing new derivatives as potential HDAC6 inhibitors.
Poonia P; Sharma M; Jha P; Chopra M
Mol Divers; 2023 Oct; 27(5):2053-2071. PubMed ID: 36214962
[TBL] [Abstract][Full Text] [Related]
9. Potential inhibitors for FKBP51: an
Barge S; Jade D; Ayyamperumal S; Manna P; Borah J; Nanjan CMJ; Nanjan MJ; Talukdar NC
J Biomol Struct Dyn; 2022; 40(24):13799-13811. PubMed ID: 34709133
[TBL] [Abstract][Full Text] [Related]
10. Pharmacophore-based virtual screening, molecular docking and molecular dynamics studies for the discovery of novel neuraminidase inhibitors.
Lotfi B; Mebarka O; Khan SU; Htar TT
J Biomol Struct Dyn; 2024 Jul; 42(10):5308-5320. PubMed ID: 37334701
[TBL] [Abstract][Full Text] [Related]
11. Search for potentially biased epidermal growth factor receptor (EGFR) inhibitors through pharmacophore modelling, molecular docking, and molecular dynamics (MD) simulation approaches.
Jethwa M; Gangopadhyay A; Saha A
J Biomol Struct Dyn; 2023 Mar; 41(5):1681-1689. PubMed ID: 35014597
[TBL] [Abstract][Full Text] [Related]
12. Pharmacophore- based virtual screening, 3D- QSAR, molecular docking approach for identification of potential dipeptidyl peptidase IV inhibitors.
Shah BM; Modi P; Trivedi P
J Biomol Struct Dyn; 2021 Apr; 39(6):2021-2043. PubMed ID: 32242496
[TBL] [Abstract][Full Text] [Related]
13. Identification of selective MMP-9 inhibitors through multiple e-pharmacophore, ligand-based pharmacophore, molecular docking, and density functional theory approaches.
Jana S; Singh SK
J Biomol Struct Dyn; 2019 Mar; 37(4):944-965. PubMed ID: 29475408
[TBL] [Abstract][Full Text] [Related]
14. Pharmacophore-based virtual screening, 3D QSAR, Docking, ADMET, and MD simulation studies: An in silico perspective for the identification of new potential HDAC3 inhibitors.
Lanka G; Begum D; Banerjee S; Adhikari N; P Y; Ghosh B
Comput Biol Med; 2023 Nov; 166():107481. PubMed ID: 37741229
[TBL] [Abstract][Full Text] [Related]
15. Identification of novel inhibitors for TNFα, TNFR1 and TNFα-TNFR1 complex using pharmacophore-based approaches.
Saddala MS; Huang H
J Transl Med; 2019 Jul; 17(1):215. PubMed ID: 31266509
[TBL] [Abstract][Full Text] [Related]
16. Identification of novel human nicotinamide N-methyltransferase inhibitors: a structure-based pharmacophore modeling and molecular dynamics approach.
Harikrishna AS; Venkitasamy K
J Biomol Struct Dyn; 2023; 41(24):14638-14650. PubMed ID: 36856058
[TBL] [Abstract][Full Text] [Related]
17. Discovery of novel VEGFR2-TK inhibitors by phthalimide pharmacophore based virtual screening, molecular docking, MD simulation and DFT.
Matore BW; Roy PP; Singh J
J Biomol Struct Dyn; 2023; 41(22):13056-13077. PubMed ID: 36775656
[TBL] [Abstract][Full Text] [Related]
18. Discovery of novel hit compounds as potential HDAC1 inhibitors: The case of ligand- and structure-based virtual screening.
Sirous H; Campiani G; Calderone V; Brogi S
Comput Biol Med; 2021 Oct; 137():104808. PubMed ID: 34478925
[TBL] [Abstract][Full Text] [Related]
19. Targeting the NF-κB/IκBα complex via fragment-based E-Pharmacophore virtual screening and binary QSAR models.
Kanan T; Kanan D; Erol I; Yazdi S; Stein M; Durdagi S
J Mol Graph Model; 2019 Jan; 86():264-277. PubMed ID: 30415122
[TBL] [Abstract][Full Text] [Related]
20. Pharmacophore modeling and virtual screening in search of novel Bruton's tyrosine kinase inhibitors.
Sharma A; Thelma BK
J Mol Model; 2019 Jun; 25(7):179. PubMed ID: 31172362
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
