Artificial intelligence catalyzes antimicrobial peptide design

Area of Science:

• Biotechnology
• Computational Biology
• Drug Discovery

Background:

• Antimicrobial peptides (AMPs) show promise against drug-resistant pathogens due to their broad-spectrum, low-resistance, and multifunctional properties.
• Discovering and optimizing AMPs is challenging due to complex sequence-function relationships.

Purpose of the Study:

• To provide an overview of AI-driven technologies for AMP design.
• To survey identification-oriented and generation-oriented AI approaches for AMP discovery.
• To highlight current challenges, limitations, and future prospects in AI-accelerated AMP development.

Main Methods:

• Utilizing artificial intelligence (AI) and machine learning strategies to build data-driven models.
• Learning latent representations from peptide sequences and biological properties for prediction.
• Developing AI models for both identifying antimicrobial activity and generating novel AMP candidates.

Main Results:

• AI enables de novo peptide design by predicting physically plausible and biologically relevant candidates.
• AI enhances the likelihood of designing AMPs with improved therapeutic potential.
• AI reduces the need for resource-intensive trial-and-error processes in AMP discovery.

Conclusions:

• AI significantly advances the development of next-generation therapeutics by optimizing AMP design.
• AI-driven approaches offer a transformative impact on combating drug-resistant pathogens.
• Future directions include finer-grained exploration, model-driven data enrichment, and model enhancement for improved AMP development.