Contemporary data-driven innovations in peptide-based therapeutic design.

Area of Science:

• Biotechnology and Pharmaceutical Sciences
• Computational Biology and Bioinformatics

Background:

• Peptides are vital in biological processes and industrial applications due to their activity, low toxicity, and specificity.
• Traditional laboratory peptide synthesis faces challenges with cost and time, limiting large-scale production.

Purpose of the Study:

• To review artificial intelligence (AI)-driven methodologies for peptide design and therapeutic development.
• To highlight how AI overcomes limitations in peptide structure, stability, and delivery.

Main Methods:

• Exploration of classical machine learning (ML) algorithms for predicting peptide properties.
• Examination of deep generative models, reinforcement learning (RL), and large language models (LLMs) in peptide design.
• Analysis of structure-aware and sequence-based AI frameworks.

Main Results:

• AI significantly reduces the number of experiments required for peptide discovery and optimization.
• AI enables accurate prediction of peptide structure-activity relationships and drug-like properties.
• AI-driven approaches accelerate the development of peptide therapeutics for oncology, metabolic disorders, and infectious diseases.

Conclusions:

• AI integration represents a paradigm shift in drug discovery, moving beyond small organic molecules to peptide-based therapeutics.
• Despite data limitations, AI combined with experimental automation promises clinical translation of novel peptide drugs.
• AI is ushering in a new era of precision peptide therapeutics with transformative potential.