154 related articles for article (PubMed ID: 37182359)
1. Developing new PI3Kγ inhibitors by combining pharmacophore modeling, molecular dynamic simulation, molecular docking, fragment-based drug design, and virtual screening.
Zhu J; Sun D; Li X; Jia L; Cai Y; Chen Y; Jin J; Yu L
Comput Biol Chem; 2023 Jun; 104():107879. PubMed ID: 37182359
[TBL] [Abstract][Full Text] [Related]
2. Developing a Naïve Bayesian Classification Model with PI3Kγ structural features for virtual screening against PI3Kγ: Combining molecular docking and pharmacophore based on multiple PI3Kγ conformations.
Jiang Y; Xiong W; Jia L; Xu L; Cai Y; Chen Y; Jin J; Gao M; Zhu J
Eur J Med Chem; 2022 Dec; 244():114824. PubMed ID: 36257282
[TBL] [Abstract][Full Text] [Related]
3. Exploring PI3Kγ binding preference with Eganelisib, Duvelisib, and Idelalisib via energetic, pharmacophore and dissociation pathway analyses.
Jia L; Wang L; Jiang Y; Xu L; Cai Y; Chen Y; Jin J; Sun H; Zhu J
Comput Biol Med; 2022 Aug; 147():105642. PubMed ID: 35635904
[TBL] [Abstract][Full Text] [Related]
4. Rational Design of Novel Phosphoinositide 3-Kinase Gamma (PI3Kγ) Selective Inhibitors: A Computational Investigation Integrating 3D-QSAR, Molecular Docking and Molecular Dynamics Simulation.
Li K; Zhu J; Xu L; Jin J
Chem Biodivers; 2019 Jul; 16(7):e1900105. PubMed ID: 31111650
[TBL] [Abstract][Full Text] [Related]
5. Discovery of novel selective PI3Kγ inhibitors through combining machine learning-based virtual screening with multiple protein structures and bio-evaluation.
Zhu J; Li K; Xu L; Cai Y; Chen Y; Zhao X; Li H; Huang G; Jin J
J Adv Res; 2022 Feb; 36():1-13. PubMed ID: 35127160
[TBL] [Abstract][Full Text] [Related]
6. A multi-conformational virtual screening approach based on machine learning targeting PI3Kγ.
Zhu J; Jiang Y; Jia L; Xu L; Cai Y; Chen Y; Zhu N; Li H; Jin J
Mol Divers; 2021 Aug; 25(3):1271-1282. PubMed ID: 34160714
[TBL] [Abstract][Full Text] [Related]
7. Identification of potential PKC inhibitors through pharmacophore designing, 3D-QSAR and molecular dynamics simulations targeting Alzheimer's disease.
Iqbal S; Anantha Krishnan D; Gunasekaran K
J Biomol Struct Dyn; 2018 Nov; 36(15):4029-4044. PubMed ID: 29182053
[TBL] [Abstract][Full Text] [Related]
8. Ligand-based 3D pharmacophore modeling, virtual screening, and molecular dynamic simulation of potential smoothened inhibitors.
Mohebbi A
J Mol Model; 2023 Apr; 29(5):143. PubMed ID: 37062794
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Identification of potential inhibitors for HCV NS3 genotype 4a by combining protein-ligand interaction fingerprint, 3D pharmacophore, docking, and dynamic simulation.
El-Hasab MAE; El-Bastawissy EE; El-Moselhy TF
J Biomol Struct Dyn; 2018 May; 36(7):1713-1727. PubMed ID: 28531373
[TBL] [Abstract][Full Text] [Related]
11. Ligand-based and e-pharmacophore modeling, 3D-QSAR and hierarchical virtual screening to identify dual inhibitors of spleen tyrosine kinase (Syk) and janus kinase 3 (JAK3).
Kaur M; Silakari O
J Biomol Struct Dyn; 2017 Nov; 35(14):3043-3060. PubMed ID: 27678281
[TBL] [Abstract][Full Text] [Related]
12. Lead generation of cysteine based mesenchymal epithelial transition (c-Met) kinase inhibitors: Using structure-based scaffold hopping, 3D-QSAR pharmacophore modeling, virtual screening, molecular docking, and molecular dynamics simulation.
Raafat A; Mowafy S; Abouseri SM; Fouad MA; Farag NA
Comput Biol Med; 2022 Jul; 146():105526. PubMed ID: 35487125
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15. Pharmacophore development, drug-likeness analysis, molecular docking, and molecular dynamics simulations for identification of new CK2 inhibitors.
Hammad S; Bouaziz-Terrachet S; Meghnem R; Meziane D
J Mol Model; 2020 May; 26(6):160. PubMed ID: 32472293
[TBL] [Abstract][Full Text] [Related]
16. Combination of Docking-Based and Pharmacophore-Based Virtual Screening Identifies Novel Agonists That Target the Urotensin Receptor.
Li N; Yin L; Chen X; Shang J; Liang M; Gao L; Qiang G; Xia J; Du G; Yang X
Molecules; 2022 Dec; 27(24):. PubMed ID: 36557826
[TBL] [Abstract][Full Text] [Related]
17. Insight into the selective mechanism of phosphoinositide 3-kinase γ with benzothiazole and thiazolopiperidine γ-specific inhibitors by in silico approaches.
Zhu J; Li K; Xu L; Jin J
Chem Biol Drug Des; 2019 May; 93(5):818-831. PubMed ID: 30582283
[TBL] [Abstract][Full Text] [Related]
18. Molecular dynamics and integrated pharmacophore-based identification of dual [Formula: see text] inhibitors.
Kaur M; Singh PK; Singh M; Bahadur R; Silakari O
Mol Divers; 2018 Feb; 22(1):95-112. PubMed ID: 29138965
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Ligand-based 3-D pharmacophore generation and molecular docking of mTOR kinase inhibitors.
Tanneeru K; Guruprasad L
J Mol Model; 2012 Apr; 18(4):1611-24. PubMed ID: 21805127
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]