Login Register Cart 0
Anti-Inflammatory & Anti-Allergy Agents in Medicinal Chemistry

Anti-Inflammatory & Anti-Allergy Agents in Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1871-5230
ISSN (Online): 1875-614X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Editorial

Translational Horizons in Computer-Aided Drug Discovery: Bridging In Silico Insights with One Health Challenges

Author(s): Manos C. Vlasiou*

Volume 24, Issue 4, 2025

Published on: 31 July, 2025

Page: [221 - 224] Pages: 4

DOI: 10.2174/0118715230424575250729093150

Become a Editorial Board Member
Become a Reviewer
Become a Editor
Become a Section Editor
Next »
[1]
Popa, S.L.; Pop, C.; Dita, M.O.; Brata, V.D.; Bolchis, R.; Czako, Z.; Saadani, M.M.; Ismaiel, A.; Dumitrascu, D.I.; Grad, S.; David, L.; Cismaru, G.; Padureanu, A.M. Deep learning and antibiotic resistance. Antibiotics, 2022, 11(11), 1674.
[http://dx.doi.org/10.3390/antibiotics11111674] [PMID: 36421316]
[2]
McPhillie, M.J.; Cain, R.M.; Narramore, S.; Fishwick, C.W.G.; Simmons, K.J. Computational methods to identify new antibacterial targets. Chem. Biol. Drug Des., 2015, 85(1), 22-29.
[http://dx.doi.org/10.1111/cbdd.12385] [PMID: 24974974]
[3]
dos Santos Nascimento, I.J.; da Silva Rodrigues, É.E.; da Silva, M.F.; de Araújo-Júnior, J.X.; de Moura, R.O. Advances in computational methods to discover new ns2b-ns3 inhibitors useful against dengue and zika viruses. Curr. Top. Med. Chem., 2022, 22(29), 2435-2462.
[http://dx.doi.org/10.2174/1568026623666221122121330] [PMID: 36415099]
[4]
Gallo, F.N.; Enderle, A.G.; Pardo, L.A.; Leal, E.S.; Bollini, M. Challenges and perspectives in the discovery of dengue virus entry inhibitors. Curr. Med. Chem., 2022, 29(4), 719-740.
[http://dx.doi.org/10.2174/0929867328666210521213118] [PMID: 34036904]
[5]
Shi, C.; Chen, J.; Kang, X.; Shen, X.; Lao, X.; Zheng, H. Approaches for the discovery of metallo‐β‐lactamase inhibitors: A review. Chem. Biol. Drug Des., 2019, 94(2), 1427-1440.
[http://dx.doi.org/10.1111/cbdd.13526] [PMID: 30925023]
[6]
Panwar, U.; Chandra, I.; Selvaraj, C.; Singh, S.K. Current computational approaches for the development of anti-HIV inhibitors: An overview. Curr. Pharm. Des., 2019, 25(31), 3390-3405.
[http://dx.doi.org/10.2174/1381612825666190911160244] [PMID: 31538884]
[7]
Hu, J.P.; Wu, Z.X.; Xie, T.; Liu, X.Y.; Yan, X.; Sun, X.; Liu, W.; Liang, L.; He, G.; Gan, Y.; Gou, X.J.; Shi, Z.; Zou, Q.; Wan, H.; Shi, H.B.; Chang, S. Applications of molecular simulation in the discovery of antituberculosis drugs: A review. Protein Pept. Lett., 2019, 26(9), 648-663.
[http://dx.doi.org/10.2174/0929866526666190620145919] [PMID: 31218945]
[8]
Tiwari, P.; Khare, T.; Shriram, V.; Bae, H.; Kumar, V. Plant synthetic biology for producing potent phyto-antimicrobials to combat antimicrobial resistance. Biotechnol. Adv., 2021, 48, 107729.
[http://dx.doi.org/10.1016/j.biotechadv.2021.107729] [PMID: 33705914]
[9]
Santos, P.; López-Vallejo, F.; Soto, C.Y. In silico approaches and chemical space of anti‐P‐type ATPase compounds for discovering new antituberculous drugs. Chem. Biol. Drug Des., 2017, 90(2), 175-187.
[http://dx.doi.org/10.1111/cbdd.12950] [PMID: 28111912]
[10]
Torres, M.D.T.; de la Fuente-Nunez, C. Toward computer-made artificial antibiotics. Curr. Opin. Microbiol., 2019, 51, 30-38.
[http://dx.doi.org/10.1016/j.mib.2019.03.004] [PMID: 31082661]
[11]
Onyango, O.H. In silico models for anti-COVID-19 drug discovery: A systematic review. Adv. Pharmacol. Pharmaceut Sci., 2023, 2023, 4562974.
[http://dx.doi.org/10.1155/2023/4562974]
[12]
Ejalonibu, M.A.; Ogundare, S.A.; Elrashedy, A.A.; Lawal, M.M.; Mhlongo, N.N.; Kumalo, H.M. Drug discovery for Mycobacterium tuberculosis using a structure-based computer-aided drug design approach. Int. J. Mol. Sci., 2021, 22(24), 13259.
[http://dx.doi.org/10.3390/ijms222413259] [PMID: 34948055]
[13]
Jujjavarapu, S.E.; Dhagat, S. In silico discovery of novel ligands for antimicrobial lipopeptides for computer-aided drug design. Probiotics Antimicrob. Proteins, 2018, 10(2), 129-141.
[http://dx.doi.org/10.1007/s12602-017-9356-9] [PMID: 29218506]
[14]
Davis, A.M.; Riley, R.J. Predictive ADMET studies, the challenges and the opportunities. Curr. Opin. Chem. Biol., 2004, 8(4), 378-386.
[http://dx.doi.org/10.1016/j.cbpa.2004.06.005] [PMID: 15288247]
[15]
Diniz, R.C.; Soares, L.W.; Nascimento da Silva, L.C. Virtual screening for the development of new effective compounds against Staphylococcus aureus. Curr. Med. Chem., 2019, 25(42), 5975-5985.
[http://dx.doi.org/10.2174/0929867325666180327105842] [PMID: 29589530]
[16]
Jukič, M.; Bren, U. Machine learning in antibacterial drug design. Front. Pharmacol., 2022, 13, 864412.
[http://dx.doi.org/10.3389/fphar.2022.864412] [PMID: 35592425]
[17]
Sabbatini, G.P.; Shirley, W.A.; Coffen, D.L. The integration of high throughput technologies for drug discovery. J. Biomol. Screen., 2001, 6(4), 213-218.
[http://dx.doi.org/10.1177/108705710100600402]
[18]
Dutertre, S.; Nicke, A.; Tsetlin, V.I. Nicotinic acetylcholine receptor inhibitors derived from snake and snail venoms. Neuropharmacology, 2017, 127, 196-223.
[http://dx.doi.org/10.1016/j.neuropharm.2017.06.011] [PMID: 28623170]
[19]
Shin, S.; Kulatunga, D.C.M.; Dananjaya, S.H.S.; Nikapitiya, C.; Lee, J.; De Zoysa, M. Saprolegnia parasitica isolated from rainbow trout in Korea: Characterization, anti- Saprolegnia activity and host pathogen interaction in zebrafish disease model. Mycobiology, 2017, 45(4), 297-311.
[http://dx.doi.org/10.5941/MYCO.2017.45.4.297] [PMID: 29371797]
[20]
Gupta, R.; Verma, R.; Pradhan, D.; Jain, A.K.; Umamaheswari, A.; Rai, C.S. An in silico approach towards identification of novel drug targets in pathogenic species of Leptospira. PLoS One, 2019, 14(8), 0221446.
[http://dx.doi.org/10.1371/journal.pone.0221446] [PMID: 31430340]

Rights & Permissions Print Cite
Article Metrics
Pdf icon 16
Html icon 2
Find your institution