Targeting the Undruggable: Deep Learning-Driven Design of Peptide Therapeutics in Cancer

Area of Science:

• Drug Discovery
• Computational Biology
• Protein Science

Background:

• Many disease-associated proteins are
• undruggable
• due to complex structures, limiting conventional drug development.
• Proteins like KRAS, p53, and c-MYC exemplify these challenges.
• Peptide therapeutics, particularly macrocyclic peptides, offer a novel approach to target these difficult proteins and protein-protein interactions (PPIs).

Purpose of the Study:

• To explore the challenges posed by
• undruggable
• proteins in disease.
• To highlight the potential of peptide therapeutics in overcoming these limitations.
• To discuss the transformative role of artificial intelligence (AI) in rational peptide design.

Main Methods:

• Reviewing structural and biological challenges of
• undruggable
• proteins.
• Analyzing the capabilities of peptide therapeutics, including macrocyclic peptides.
• Examining the application of AI and deep learning models, such as diffusion-based generative models, for de novo peptide design.

Main Results:

• Peptide therapeutics can effectively target challenging protein-protein interactions (PPIs) where small molecules fail.
• AI enables the rational design of high-affinity peptide binders with structural precision for previously intractable targets.
• AI transforms peptide discovery from empirical screening to a design-driven process.

Conclusions:

• Peptide-based modalities are crucial for addressing
• undruggable
• targets.
• AI and computational modeling are reshaping the design of next-generation peptide therapeutics.
• An integrated experimental-computational framework is proposed to accelerate the development of clinically viable peptide candidates.