149 related articles for article (PubMed ID: 36257282)
1. 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]
2. 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]
3. 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]
4. 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]
5. Optimization of virtual screening against phosphoinositide 3-kinase delta: Integration of common feature pharmacophore and multicomplex-based molecular docking.
Zhu J; Meng H; Li X; Jia L; Xu L; Cai Y; Chen Y; Jin J; Yu L
Comput Biol Chem; 2024 Apr; 109():108011. PubMed ID: 38198965
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Multiple receptor-ligand based pharmacophore modeling and molecular docking to screen the selective inhibitors of matrix metalloproteinase-9 from natural products.
Gao Q; Wang Y; Hou J; Yao Q; Zhang J
J Comput Aided Mol Des; 2017 Jul; 31(7):625-641. PubMed ID: 28623487
[TBL] [Abstract][Full Text] [Related]
8. Discovery of a novel phosphoinositide 3-kinase gamma (PI3Kγ) inhibitor against hematologic malignancies and theoretical studies on its PI3Kγ-specific binding mechanisms.
Zhu J; Ke K; Xu L; Jin J
RSC Adv; 2019 Jun; 9(35):20207-20215. PubMed ID: 35546906
[TBL] [Abstract][Full Text] [Related]
9. Development and evaluation of an integrated virtual screening strategy by combining molecular docking and pharmacophore searching based on multiple protein structures.
Tian S; Sun H; Li Y; Pan P; Li D; Hou T
J Chem Inf Model; 2013 Oct; 53(10):2743-56. PubMed ID: 24010823
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Exploration of the structural requirements of HIV-protease inhibitors using pharmacophore, virtual screening and molecular docking approaches for lead identification.
Islam MA; Pillay TS
J Mol Graph Model; 2015 Mar; 56():20-30. PubMed ID: 25541527
[TBL] [Abstract][Full Text] [Related]
12. Screening for the selective inhibitors of MMP-9 from natural products based on pharmacophore modeling and molecular docking in combination with bioassay experiment, hybrid QM/MM calculation, and MD simulation.
Hou J; Zou Q; Wang Y; Gao Q; Yao W; Yao Q; Zhang J
J Biomol Struct Dyn; 2019 Aug; 37(12):3135-3149. PubMed ID: 30079817
[TBL] [Abstract][Full Text] [Related]
13. Discovery of nanomolar phosphoinositide 3-kinase gamma (PI3Kγ) inhibitors using ligand-based modeling and virtual screening followed by in vitro analysis.
Taha MO; Al-Sha'er MA; Khanfar MA; Al-Nadaf AH
Eur J Med Chem; 2014 Sep; 84():454-65. PubMed ID: 25050878
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. 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]
16. Integrating Machine Learning-Based Virtual Screening With Multiple Protein Structures and Bio-Assay Evaluation for Discovery of Novel GSK3β Inhibitors.
Zhu J; Wu Y; Wang M; Li K; Xu L; Chen Y; Cai Y; Jin J
Front Pharmacol; 2020; 11():566058. PubMed ID: 33041806
[TBL] [Abstract][Full Text] [Related]
17. Targeting phosphatidylinositol 3-kinase gamma (PI3Kγ): Discovery and development of its selective inhibitors.
Zhu J; Li K; Yu L; Chen Y; Cai Y; Jin J; Hou T
Med Res Rev; 2021 May; 41(3):1599-1621. PubMed ID: 33300614
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Evolutionary chemical binding similarity approach integrated with 3D-QSAR method for effective virtual screening.
Durai P; Ko YJ; Pan CH; Park K
BMC Bioinformatics; 2020 Jul; 21(1):309. PubMed ID: 32664863
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]