Peptide–Protein Docking: From Physics-Based Models to Generative Intelligence

Kai Ling; Shu Li; Zicong Zhang; Woong‐Hee Shin; Daisuke Kihara

doi:10.1039/d6cc00583g

January 1, 2026Chemical CommunicationsOpen Access

Peptide–Protein Docking: From Physics-Based Models to Generative Intelligence

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Key Points

The central aim is to understand peptide-protein interactions and improve complex structure determination.
Utilized physics-based models for peptide-protein docking.
Applied generative intelligence approaches.
Analyzed the impact on signaling pathways and cellular regulation.
Identified significant peptide-protein interactions that influence cellular signaling.
Demonstrated cost-effectiveness compared to traditional experimental methods.
Showed improved predictions of complex structures with generative models.

Implication

Explores peptide-protein interactions and highlights their role in signaling and therapeutics.

Abstract

Peptide–protein interactions (PepPIs) play a pivotal role in cellular signaling and regulation, representing a significant category of therapeutic agents. However, determining peptide–protein complex structures by experiment is costly and often...

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Cite This Study

Ling et al. (2026) studied this question.

synapsesocial.com/papers/69be36af6e48c4981c675b7f https://doi.org/10.1039/d6cc00583g

View Full Paper

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Peptide–Protein Docking: From Physics-Based Models to Generative Intelligence

Key Points

Implication

Abstract

Discussion

Cite This Study

Peptide–Protein Docking: From Physics-Based Models to Generative Intelligence

Key Points

Implication

Abstract

Discussion

Cite This Study

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