AIMC Topic: Antimicrobial Peptides
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AAGP integrates physicochemical and compositional features for machine learning-based prediction of anti-aging peptides.
Scientific reports
Aug 8, 2025
Aging is a natural phenomenon characterized by the loss of normal morphology and physiological functioning of the body, causing wrinkles on the skin, loss of hair, and compromised immune systems. Peptide therapies have emerged as a promising approach...
Computational exploration of global venoms for antimicrobial discovery with Venomics artificial intelligence.
Nature communications
Jul 12, 2025
The rise of antibiotic-resistant pathogens, particularly gram-negative bacteria, highlights the urgent need for novel therapeutics. Drug-resistant infections now contribute to approximately 5 million deaths annually, yet traditional antibiotic discov...
Antimicrobial Peptides Design Using Deep Learning and Rational Modifications: Activity in Bacteria, Candida albicans, and Cancer Cells.
Current microbiology
Jul 11, 2025
Resistance to antimicrobial agents has become a global threat, estimated to cause 10-million deaths annually by 2050. Antimicrobial peptides are emerging as an alternative and offer advantages over traditional antibiotics. Antimicrobial peptides gene...
Deep Learning in Antimicrobial Peptide Prediction.
Journal of chemical information and modeling
Jul 8, 2025
Antimicrobial peptides (AMPs) have garnered significant attention from researchers as effective alternatives to antibiotics. In recent years, deep learning has demonstrated unique advantages in AMP prediction, surpassing traditional machine learning ...
AI-Accelerated Identification of Novel Antimicrobial Peptides for Inhibiting .
Journal of agricultural and food chemistry
Jul 1, 2025
Fusarium head blight caused by threatens global wheat production, causing substantial yield reduction and mycotoxin accumulation. This study harnessed machine learning to accelerate the discovery of antifungal peptides targeting this phytopathogen. ...
Disruption of Hsp70.14-BAG2 Protein-Protein interactions using deep Learning-Driven peptide design and molecular simulations.
Computers in biology and medicine
Jun 3, 2025
Protein-protein interactions (PPIS) are critical in proteostasis, stress response, and disease progression. Targeting the interaction between Hsp70.14 and BAG2, a co-chaperone implicated in oncogenic survival, offers a promising therapeutic approach....
AI-Driven Antimicrobial Peptide Discovery: Mining and Generation.
Accounts of chemical research
Jun 3, 2025
ConspectusThe escalating threat of antimicrobial resistance (AMR) poses a significant global health crisis, potentially surpassing cancer as a leading cause of death by 2050. Traditional antibiotic discovery methods have not kept pace with the rapidl...
AMPGen: an evolutionary information-reserved and diffusion-driven generative model for de novo design of antimicrobial peptides.
Communications biology
May 30, 2025
The rapid advancement of artificial intelligence (AI) has enabled de novo design of functional proteins, circumventing the reliance on natural templates or sequencing databases. However, current protein design models are ineffective in generating pro...
Antifungal activity and mechanism of novel peptide antimicrobial peptide (GmAMP) against fluconazole-resistant .
PeerJ
May 20, 2025
BACKGROUND: There is a pressing need to create innovative alternative treatment approaches considering the overuse of antifungal drugs causes the number of clinically isolated fluconazole-resistant species to increase. antimicrobial peptide (GmAMP)...
De novo design of self-assembling peptides with antimicrobial activity guided by deep learning.
Nature materials
Mar 14, 2025
Bioinspired materials based on self-assembling peptides are promising for tackling various challenges in biomedical engineering. While contemporary data-driven approaches have led to the discovery of self-assembling peptides with various structures a...
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