AIMC Topic: Antimicrobial Peptides
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BERT-AmPEP60: A BERT-Based Transfer Learning Approach to Predict the Minimum Inhibitory Concentrations of Antimicrobial Peptides for and .
Journal of chemical information and modeling
Mar 14, 2025
Antimicrobial peptides (AMPs) are a promising alternative for combating bacterial drug resistance. While current computer prediction models excel at binary classification of AMPs based on sequences, there is a lack of regression methods to accurately...
Exploring the repository of de novo-designed bifunctional antimicrobial peptides through deep learning.
eLife
Mar 13, 2025
Antimicrobial peptides (AMPs) are attractive candidates to combat antibiotic resistance for their capability to target biomembranes and restrict a wide range of pathogens. It is a daunting challenge to discover novel AMPs due to their sparse distribu...
MDTL-ACP: Anticancer Peptides Prediction Based on Multi-Domain Transfer Learning.
IEEE journal of biomedical and health informatics
Mar 6, 2025
Anticancer peptides (ACPs) have emerged as one of the most promising therapeutic agents for cancer treatment. They are bioactive peptides featuring broad-spectrum activity and low drug-resistance. The discovery of ACPs via traditional biochemical met...
ML-AMPs designed through machine learning show antifungal activity against C. albicans and therapeutic potential on mice model with candidiasis.
Life sciences
Feb 19, 2025
AIMS: C. albicans resistant strains have led to increasingly severe treatment challenges. Antimicrobial peptides with low resistance-inducing propensity for pathogens have been developed. A series of antimicrobial peptides de novo designed through ma...
Deep Learning for Antimicrobial Peptides: Computational Models and Databases.
Journal of chemical information and modeling
Feb 10, 2025
Antimicrobial peptides are a promising strategy to combat antimicrobial resistance. However, the experimental discovery of antimicrobial peptides is both time-consuming and laborious. In recent years, the development of computational technologies (es...
MSCMamba: Prediction of Antimicrobial Peptide Activity Values by Fusing Multiscale Convolution with Mamba Module.
The journal of physical chemistry. B
Feb 6, 2025
Antimicrobial peptides (AMPs) have important developmental prospects as potential candidates for novel antibiotics. Although many studies have been devoted to the identification of AMPs and the qualitative prediction of their functional activities, f...
Artificial intelligence using a latent diffusion model enables the generation of diverse and potent antimicrobial peptides.
Science advances
Feb 5, 2025
Artificial intelligence holds great promise for the design of antimicrobial peptides (AMPs); however, current models face limitations in generating AMPs with sufficient novelty and diversity, and they are rarely applied to the generation of antifunga...
Unlocking Antimicrobial Peptides: In Silico Proteolysis and Artificial Intelligence-Driven Discovery from Cnidarian Omics.
Molecules (Basel, Switzerland)
Jan 25, 2025
Overcoming the growing challenge of antimicrobial resistance (AMR), which affects millions of people worldwide, has driven attention for the exploration of marine-derived antimicrobial peptides (AMPs) for innovative solutions. Cnidarians, such as cor...
Explainable deep learning and virtual evolution identifies antimicrobial peptides with activity against multidrug-resistant human pathogens.
Nature microbiology
Jan 17, 2025
Artificial intelligence (AI) is a promising approach to identify new antimicrobial compounds in diverse microbial species. Here we developed an AI-based, explainable deep learning model, EvoGradient, that predicts the potency of antimicrobial peptide...
UniAMP: enhancing AMP prediction using deep neural networks with inferred information of peptides.
BMC bioinformatics
Jan 11, 2025
Antimicrobial peptides (AMPs) have been widely recognized as a promising solution to combat antimicrobial resistance of microorganisms due to the increasing abuse of antibiotics in medicine and agriculture around the globe. In this study, we propose ...
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