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Novel Antimicrobials from Computational Modelling and Drug Repositioning: Potential In Silico Strategies to Increase Therapeutic Arsenal Against Antimicrobial Resistance
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Área de Microbiología, Departamento de Farmacia, Instituto de Ciencias Biomédicas, Facultad de Ciencias de la Salud, Universidad Cardenal Herrera-CEU, CEU Universities, Alfara del Patriarca, 46115 Valencia, Spain
2
ESI International Chair@CEU-UCH, Departamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera-CEU, CEU Universities, C/San Bartolomé 55, Alfara del Patriarca, 46115 Valencia, Spain
*
Author to whom correspondence should be addressed.
Molecules 2025, 30(11), 2303; https://doi.org/10.3390/molecules30112303 (registering DOI)
Submission received: 18 April 2025
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Revised: 17 May 2025
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Accepted: 22 May 2025
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Published: 24 May 2025
Abstract
Antimicrobial resistance (AMR) is one of the most significant public health threats today. The need for new antimicrobials against multidrug-resistant infections is growing. The development of computational models capable of predicting new drug–target interactions is an interesting strategy to reposition already known drugs into potential antimicrobials. The objective of this review was to compile the latest advances in the development of computational models capable of identifying drugs already registered by the Food and Drug Administration for other indications with potential capacity to be applied as antimicrobials. We present studies that apply in silico methods such as machine learning, molecular docking, molecular dynamics and deep learning. Some of these studies have in vitro/in vivo results that demonstrate the reliability of this computational methodology in terms of the identification of effective molecules and new targets of interest in the treatment of infections. In addition, we present the methods that are under development and their future prospects in terms of the search for new antimicrobials. We highlight the need to implement these strategies in the research of effective drugs in the treatment of infectious diseases and to continue to improve the available models and approaches to gain an advantage against the rapid emergence of AMR.
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MDPI and ACS Style
Tarín-Pelló, A.; Fernández-Álvarez, S.; Suay-García, B.; Pérez-Gracia, M.T.
Novel Antimicrobials from Computational Modelling and Drug Repositioning: Potential In Silico Strategies to Increase Therapeutic Arsenal Against Antimicrobial Resistance. Molecules 2025, 30, 2303.
https://doi.org/10.3390/molecules30112303
AMA Style
Tarín-Pelló A, Fernández-Álvarez S, Suay-García B, Pérez-Gracia MT.
Novel Antimicrobials from Computational Modelling and Drug Repositioning: Potential In Silico Strategies to Increase Therapeutic Arsenal Against Antimicrobial Resistance. Molecules. 2025; 30(11):2303.
https://doi.org/10.3390/molecules30112303
Chicago/Turabian Style
Tarín-Pelló, Antonio, Sara Fernández-Álvarez, Beatriz Suay-García, and María Teresa Pérez-Gracia.
2025. "Novel Antimicrobials from Computational Modelling and Drug Repositioning: Potential In Silico Strategies to Increase Therapeutic Arsenal Against Antimicrobial Resistance" Molecules 30, no. 11: 2303.
https://doi.org/10.3390/molecules30112303
APA Style
Tarín-Pelló, A., Fernández-Álvarez, S., Suay-García, B., & Pérez-Gracia, M. T.
(2025). Novel Antimicrobials from Computational Modelling and Drug Repositioning: Potential In Silico Strategies to Increase Therapeutic Arsenal Against Antimicrobial Resistance. Molecules, 30(11), 2303.
https://doi.org/10.3390/molecules30112303
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