Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design.

Area of Science:

• Biochemistry
• Computational Biology
• Drug Discovery

Background:

• Peptides offer high affinity and specificity for disease targets, addressing unmet medical needs.
• Peptide design faces challenges due to smaller size, flexibility, and limited data compared to proteins.
• Advancements in deep learning present opportunities to overcome these design hurdles.

Purpose of the Study:

• To review the application of deep learning in peptide therapeutics.
• To explore methods for enhancing peptide structure prediction and design using AI.
• To guide researchers on leveraging deep learning for peptide drug development.

Main Methods:

• Dataset curation and handling for peptide research.
• Development and application of deep learning models for peptide structure prediction.
• Integration of structure-activity relationship principles in AI-driven design.

Main Results:

• Deep learning models show promise in improving peptide structure prediction accuracy.
• AI-driven approaches can accelerate the design of novel peptide therapeutics.
• The review synthesizes current challenges and future opportunities in the field.

Conclusions:

• Deep learning is a transformative technology for peptide therapeutics.
• Continued refinement of AI models will advance peptide drug discovery.
• Addressing data limitations and model interpretability are key future directions.