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From Prediction to Design: A Paradigm Shift in AI-Driven Protein–Protein Interaction Research

  • Yihuang Wu,   
  • Wei Hu and 
  • Linlin Zhao *

Received: 16 June 2026 | Revised: 14 August 2026 | Accepted: 17 August 2026 | Published: 23 October 2026

Abstract

Protein-protein interactions (PPIs) form the molecular foundation of cellular life. Over the past two decades, computational methods for PPI analysis have primarily focused on prediction—inferring interaction properties from protein information. However, a notable conceptual shift is underway away from predictive analysis and toward rational protein interaction design. This transformation is driven by breakthroughs in protein language models, diffusion models, and equivariant neural networks. In this review, we discuss how this transition represents a notable conceptual shift, enabling exploration of sequence-structure space that evolution has never sampled. We synthesize the technical foundations, core design tasks, and challenges ahead. We address critical issues including evaluation bias, the hallucination problem, and dual-use considerations. We conclude with grand challenges for the field: mapping design ability boundaries, designing dynamic interactions, and developing interpretable methods for clinical translation. We further describe how modern AI reshapes computational approaches for biological discovery.

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DOI
10.53941/hm.2026.100025
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Volume 3, Issue 4
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Wu, Y.; Hu, W.; Zhao, L. From Prediction to Design: A Paradigm Shift in AI-Driven Protein–Protein Interaction Research. Health and Metabolism 2026, 3 (4), 3. https://doi.org/10.53941/hm.2026.100025.
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