AI Medical Compendium Topic:
Peptides

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ACME: pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Prediction of peptide binding to the major histocompatibility complex (MHC) plays a vital role in the development of therapeutic vaccines for the treatment of cancer. Algorithms with improved correlations between predicted and actual bind...

Identifying antimicrobial peptides using word embedding with deep recurrent neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Antibiotic resistance constitutes a major public health crisis, and finding new sources of antimicrobial drugs is crucial to solving it. Bacteriocins, which are bacterially produced antimicrobial peptide products, are candidates for broad...

Identification of hormone binding proteins based on machine learning methods.

Mathematical biosciences and engineering : MBE
The soluble carrier hormone binding protein (HBP) plays an important role in the growth of human and other animals. HBP can also selectively and non-covalently interact with hormone. Therefore, accurate identification of HBP is an important prerequis...

Identification of Anti-cancer Peptides Based on Multi-classifier System.

Combinatorial chemistry & high throughput screening
AIMS AND OBJECTIVE: Cancer is one of the deadliest diseases, taking the lives of millions every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anti-cancer peptides (ACPs) can eliminate this side eff...

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery.

Current topics in medicinal chemistry
Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the pe...

Machine Learning in Quantitative Protein-peptide Affinity Prediction: Implications for Therapeutic Peptide Design.

Current drug metabolism
BACKGROUND: Protein-peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recen...

Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design.

The Journal of chemical physics
Auto-associative neural networks ("autoencoders") present a powerful nonlinear dimensionality reduction technique to mine data-driven collective variables from molecular simulation trajectories. This technique furnishes explicit and differentiable ex...

Deep learning improves antimicrobial peptide recognition.

Bioinformatics (Oxford, England)
MOTIVATION: Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by wet-laborat...