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2000
Volume 24, Issue 4
  • ISSN: 1871-5230
  • E-ISSN: 1875-614X
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2025-07-31
2026-01-05
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References

  1. PopaS.L. PopC. DitaM.O. BrataV.D. BolchisR. CzakoZ. SaadaniM.M. IsmaielA. DumitrascuD.I. GradS. DavidL. CismaruG. PadureanuA.M. Deep learning and antibiotic resistance.Antibiotics20221111167410.3390/antibiotics11111674 36421316
    [Google Scholar]
  2. McPhillieM.J. CainR.M. NarramoreS. FishwickC.W.G. SimmonsK.J. Computational methods to identify new antibacterial targets.Chem. Biol. Drug Des.2015851222910.1111/cbdd.12385 24974974
    [Google Scholar]
  3. dos Santos NascimentoI.J. da Silva RodriguesÉ.E. da SilvaM.F. de Araújo-JúniorJ.X. de MouraR.O. Advances in computational methods to discover new ns2b-ns3 inhibitors useful against dengue and zika viruses.Curr. Top. Med. Chem.202222292435246210.2174/1568026623666221122121330 36415099
    [Google Scholar]
  4. GalloF.N. EnderleA.G. PardoL.A. LealE.S. BolliniM. Challenges and perspectives in the discovery of dengue virus entry inhibitors.Curr. Med. Chem.202229471974010.2174/0929867328666210521213118 34036904
    [Google Scholar]
  5. ShiC. ChenJ. KangX. ShenX. LaoX. ZhengH. Approaches for the discovery of metallo‐β‐lactamase inhibitors: A review.Chem. Biol. Drug Des.20199421427144010.1111/cbdd.13526 30925023
    [Google Scholar]
  6. PanwarU. ChandraI. SelvarajC. SinghS.K. Current computational approaches for the development of anti-HIV inhibitors: An overview.Curr. Pharm. Des.201925313390340510.2174/1381612825666190911160244 31538884
    [Google Scholar]
  7. HuJ.P. WuZ.X. XieT. LiuX.Y. YanX. SunX. LiuW. LiangL. HeG. GanY. GouX.J. ShiZ. ZouQ. WanH. ShiH.B. ChangS. Applications of molecular simulation in the discovery of antituberculosis drugs: A review.Protein Pept. Lett.201926964866310.2174/0929866526666190620145919 31218945
    [Google Scholar]
  8. TiwariP. KhareT. ShriramV. BaeH. KumarV. Plant synthetic biology for producing potent phyto-antimicrobials to combat antimicrobial resistance.Biotechnol. Adv.20214810772910.1016/j.biotechadv.2021.107729 33705914
    [Google Scholar]
  9. SantosP. López-VallejoF. SotoC.Y. In silico approaches and chemical space of anti‐P‐type ATPase compounds for discovering new antituberculous drugs.Chem. Biol. Drug Des.201790217518710.1111/cbdd.12950 28111912
    [Google Scholar]
  10. TorresM.D.T. de la Fuente-NunezC. Toward computer-made artificial antibiotics.Curr. Opin. Microbiol.201951303810.1016/j.mib.2019.03.004 31082661
    [Google Scholar]
  11. OnyangoO.H. In silico models for anti-COVID-19 drug discovery: A systematic review.Adv. Pharmacol. Pharmaceut Sci.20232023456297410.1155/2023/4562974
    [Google Scholar]
  12. EjalonibuM.A. OgundareS.A. ElrashedyA.A. LawalM.M. MhlongoN.N. KumaloH.M. Drug discovery for Mycobacterium tuberculosis using a structure-based computer-aided drug design approach.Int. J. Mol. Sci.202122241325910.3390/ijms222413259 34948055
    [Google Scholar]
  13. JujjavarapuS.E. DhagatS. In silico discovery of novel ligands for antimicrobial lipopeptides for computer-aided drug design.Probiotics Antimicrob. Proteins201810212914110.1007/s12602‑017‑9356‑9 29218506
    [Google Scholar]
  14. DavisA.M. RileyR.J. Predictive ADMET studies, the challenges and the opportunities.Curr. Opin. Chem. Biol.20048437838610.1016/j.cbpa.2004.06.005 15288247
    [Google Scholar]
  15. DinizR.C. SoaresL.W. Nascimento da SilvaL.C. Virtual screening for the development of new effective compounds against Staphylococcus aureus.Curr. Med. Chem.201925425975598510.2174/0929867325666180327105842 29589530
    [Google Scholar]
  16. JukičM. BrenU. Machine learning in antibacterial drug design.Front. Pharmacol.20221386441210.3389/fphar.2022.864412 35592425
    [Google Scholar]
  17. SabbatiniG.P. ShirleyW.A. CoffenD.L. The integration of high throughput technologies for drug discovery.J. Biomol. Screen.20016421321810.1177/108705710100600402
    [Google Scholar]
  18. DutertreS. NickeA. TsetlinV.I. Nicotinic acetylcholine receptor inhibitors derived from snake and snail venoms.Neuropharmacology201712719622310.1016/j.neuropharm.2017.06.011 28623170
    [Google Scholar]
  19. ShinS. KulatungaD.C.M. DananjayaS.H.S. NikapitiyaC. LeeJ. De ZoysaM. Saprolegnia parasitica isolated from rainbow trout in Korea: Characterization, anti- Saprolegnia activity and host pathogen interaction in zebrafish disease model.Mycobiology201745429731110.5941/MYCO.2017.45.4.297 29371797
    [Google Scholar]
  20. GuptaR. VermaR. PradhanD. JainA.K. UmamaheswariA. RaiC.S. An in silico approach towards identification of novel drug targets in pathogenic species of Leptospira.PLoS One2019148022144610.1371/journal.pone.0221446 31430340
    [Google Scholar]
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