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Machine learning for antimicrobial peptide identification and design
Artificial intelligence (AI) and machine learning (ML) models are being deployed in many
domains of society and have recently reached the field of drug discovery. Given the …
domains of society and have recently reached the field of drug discovery. Given the …
[HTML][HTML] Recent progress in the discovery and design of antimicrobial peptides using traditional machine learning and deep learning
Antimicrobial resistance has become a critical global health problem due to the abuse of
conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides …
conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides …
ToxinPred 3.0: An improved method for predicting the toxicity of peptides
Toxicity emerges as a prominent challenge in the design of therapeutic peptides, causing
the failure of numerous peptides during clinical trials. In 2013, our group developed …
the failure of numerous peptides during clinical trials. In 2013, our group developed …
Artificial intelligence-driven antimicrobial peptide discovery
Antimicrobial peptides (AMPs) emerge as promising agents against antimicrobial resistance,
providing an alternative to conventional antibiotics. Artificial intelligence (AI) revolutionized …
providing an alternative to conventional antibiotics. Artificial intelligence (AI) revolutionized …
Bacteria-specific feature selection for enhanced antimicrobial peptide activity predictions using machine-learning methods
H Teimouri, A Medvedeva… - Journal of Chemical …, 2023 - ACS Publications
There are several classes of short peptide molecules, known as antimicrobial peptides
(AMPs), which are produced during the immune responses of living organisms against …
(AMPs), which are produced during the immune responses of living organisms against …
Prediction of antifungal activity of antimicrobial peptides by transfer learning from protein pretrained models
Peptides with antifungal activity have gained significant attention due to their potential
therapeutic applications. In this study, we explore the use of pretrained protein models as …
therapeutic applications. In this study, we explore the use of pretrained protein models as …
[HTML][HTML] The role and future prospects of artificial intelligence algorithms in peptide drug development
Z Chen, R Wang, J Guo, X Wang - Biomedicine & Pharmacotherapy, 2024 - Elsevier
Peptide medications have been more well-known in recent years due to their many benefits,
including low side effects, high biological activity, specificity, effectiveness, and so on. Over …
including low side effects, high biological activity, specificity, effectiveness, and so on. Over …
Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations
We introduce a computational approach for the design of target-specific peptides. Our
method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta …
method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta …
Antimicrobial peptides as drugs with double response against Mycobacterium tuberculosis coinfections in lung cancer
Tuberculosis and lung cancer are, in many cases, correlated diseases that can be confused
because they have similar symptoms. Many meta-analyses have proven that there is a …
because they have similar symptoms. Many meta-analyses have proven that there is a …
Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns
Long extrachromosomal circular DNA (leccDNA) regulates several biological processes
such as genomic instability, gene amplification, and oncogenesis. The identification of …
such as genomic instability, gene amplification, and oncogenesis. The identification of …