118 related articles for article (PubMed ID: 38379294)
1. Machine learning and molecular simulation-based protocols to identify novel potential inhibitors for reverse transcriptase against HIV infections.
Shahab M; Zheng G; Bin Jardan YA; Bourhia M
J Biomol Struct Dyn; 2024 Feb; ():1-14. PubMed ID: 38379294
[TBL] [Abstract][Full Text] [Related]
2. Machine learning-based drug design for identification of thymidylate kinase inhibitors as a potential anti-Mycobacterium tuberculosis.
Shahab M; Danial M; Duan X; Khan T; Liang C; Gao H; Chen M; Wang D; Zheng G
J Biomol Struct Dyn; 2024 May; 42(8):3874-3886. PubMed ID: 37232453
[TBL] [Abstract][Full Text] [Related]
3. Machine Learning-Based Virtual Screening and Molecular Simulation Approaches Identified Novel Potential Inhibitors for Cancer Therapy.
Shahab M; Zheng G; Khan A; Wei D; Novikov AS
Biomedicines; 2023 Aug; 11(8):. PubMed ID: 37626747
[TBL] [Abstract][Full Text] [Related]
4. Machine Learning-based Virtual Screening for STAT3 Anticancer Drug Target.
Wadood A; Ajmal A; Junaid M; Rehman AU; Uddin R; Azam SS; Khan AZ; Ali A
Curr Pharm Des; 2022; 28(36):3023-3032. PubMed ID: 35909285
[TBL] [Abstract][Full Text] [Related]
5. Machine learning facilitated structural activity relationship approach for the discovery of novel inhibitors targeting EGFR.
Choudhary R; Walhekar V; Muthal A; Kumar D; Bagul C; Kulkarni R
J Biomol Struct Dyn; 2023; 41(22):12445-12463. PubMed ID: 36762704
[TBL] [Abstract][Full Text] [Related]
6. Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets.
Zorn KM; Lane TR; Russo DP; Clark AM; Makarov V; Ekins S
Mol Pharm; 2019 Apr; 16(4):1620-1632. PubMed ID: 30779585
[TBL] [Abstract][Full Text] [Related]
7. An
da Costa APL; Cardoso FJB; Molfetta FA
J Biomol Struct Dyn; 2023 Mar; 41(5):1715-1729. PubMed ID: 34996334
[TBL] [Abstract][Full Text] [Related]
8. In Silico Prediction of New Inhibitors for Kirsten Rat Sarcoma G12D Cancer Drug Target Using Machine Learning-Based Virtual Screening, Molecular Docking, and Molecular Dynamic Simulation Approaches.
Ajmal A; Danial M; Zulfat M; Numan M; Zakir S; Hayat C; Alabbosh KF; Zaki MEA; Ali A; Wei D
Pharmaceuticals (Basel); 2024 Apr; 17(5):. PubMed ID: 38794122
[TBL] [Abstract][Full Text] [Related]
9. Discovering the Active Ingredients of Medicine and Food Homologous Substances for Inhibiting the Cyclooxygenase-2 Metabolic Pathway by Machine Learning Algorithms.
Tian Y; Zhang Z; Yan A
Molecules; 2023 Sep; 28(19):. PubMed ID: 37836625
[TBL] [Abstract][Full Text] [Related]
10. Virtual screening models for prediction of HIV-1 RT associated RNase H inhibition.
Poongavanam V; Kongsted J
PLoS One; 2013; 8(9):e73478. PubMed ID: 24066050
[TBL] [Abstract][Full Text] [Related]
11. Machine Learning Models Combined with Virtual Screening and Molecular Docking to Predict Human Topoisomerase I Inhibitors.
Li B; Kang X; Zhao D; Zou Y; Huang X; Wang J; Zhang C
Molecules; 2019 Jun; 24(11):. PubMed ID: 31167344
[TBL] [Abstract][Full Text] [Related]
12.
Gunasinghe J; Hwang SS; Yam WK; Rahman T; Wezen XC
J Biomol Struct Dyn; 2023; 41(12):5583-5596. PubMed ID: 35751129
[TBL] [Abstract][Full Text] [Related]
13. Molecular Docking and Molecular Dynamics to Identify a Novel Human Immunodeficiency Virus Inhibitor from Alkaloids of Toddalia asiatica.
Priya R; Sumitha R; Doss CG; Rajasekaran C; Babu S; Seenivasan R; Siva R
Pharmacogn Mag; 2015 Oct; 11(Suppl 3):S414-22. PubMed ID: 26929575
[TBL] [Abstract][Full Text] [Related]
14. Machine learning-based modeling to predict inhibitors of acetylcholinesterase.
Sandhu H; Kumar RN; Garg P
Mol Divers; 2022 Feb; 26(1):331-340. PubMed ID: 33891263
[TBL] [Abstract][Full Text] [Related]
15. A Combined Approach of Pharmacophore Modeling, QSAR Study, Molecular Docking and In silico ADME/Tox Prediction of 4-Arylthio & 4-Aryloxy-3- Iodopyridine-2(1H)-one Analogs to Identify Potential Reverse Transcriptase Inhibitor: Anti-HIV Agents.
Panigrahi D; Mishra A; Sahu SK; Azam MA; Vyshaag CM
Med Chem; 2022; 18(1):51-87. PubMed ID: 33319692
[TBL] [Abstract][Full Text] [Related]
16. Dual inhibitor design for HIV-1 reverse transcriptase and integrase enzymes: a molecular docking study.
Ercan S; Şenyiğit B; Şenses Y
J Biomol Struct Dyn; 2020 Feb; 38(2):573-580. PubMed ID: 31787027
[TBL] [Abstract][Full Text] [Related]
17. Multi-stage virtual screening of natural products against p38α mitogen-activated protein kinase: predictive modeling by machine learning, docking study and molecular dynamics simulation.
Yang R; Zha X; Gao X; Wang K; Cheng B; Yan B
Heliyon; 2022 Sep; 8(9):e10495. PubMed ID: 36105464
[TBL] [Abstract][Full Text] [Related]
18. Development and validation of consensus machine learning-based models for the prediction of novel small molecules as potential anti-tubercular agents.
Wani MA; Roy KK
Mol Divers; 2022 Jun; 26(3):1345-1356. PubMed ID: 34110578
[TBL] [Abstract][Full Text] [Related]
19. New strategy for identifying potential natural HIV-1 non-nucleoside reverse transcriptase inhibitors against drug-resistance: an
Wang Y; Wang X; Xiong Y; Kaushik AC; Muhammad J; Khan A; Dai H; Wei DQ
J Biomol Struct Dyn; 2020 Jul; 38(11):3327-3341. PubMed ID: 31422767
[TBL] [Abstract][Full Text] [Related]
20. Design of metronidazole derivatives and flavonoids as potential non-nucleoside reverse transcriptase inhibitors using combined ligand- and structure-based approaches.
Cutinho PF; Roy J; Anand A; Cheluvaraj R; Murahari M; Chimatapu HSV
J Biomol Struct Dyn; 2020 Apr; 38(6):1626-1648. PubMed ID: 31046644
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
