The evolution of computation-driven paradigms in targeted peptide drug design: From predictive modeling to generative AI and clinical translation
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PUBLIC SUMMARY
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Targeted peptides tackle undruggable targets but face in vivo instability.
Peptide design shifts from empirical screening to artificial intelligence (AI).
Generative AI creates novel sequences, bypassing limits of natural databases.
Integrating AI with physics validation accelerates clinical peptide development.
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ABSTRACT
Targeted peptide therapeutics offer a potent solution for undruggable intracellular targets yet their clinical translation remains hampered by poor membrane permeability and metabolic instability. The integration of high-performance computing and artificial intelligence is currently driving a fundamental transition from empirical screening to rational de novo design. This review moves beyond a conventional enumeration of tools to construct a strategic framework that integrates physics-based validation with generative deep learning. We critically analyze the synergistic application of molecular dynamics and docking for thermodynamic verification while simultaneously evaluating how diffusion models and protein language models accelerate the exploration of vast chemical spaces. By delineating a closed-loop workflow that incorporates pharmacokinetic constraints into generative algorithms this review not only synthesizes current advancements but also provides a strategic roadmap for seamlessly integrating generative AI with physics-based validation, thereby accelerating the transition of computationally designed peptides from in silico blueprints to viable clinical candidates. -
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REFERENCES
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Hu W., Sun Y., Li T., et al. (2026). The evolution of computation-driven paradigms in targeted peptide drug design: From predictive modeling to generative AI and clinical translation. The Innovation Drug Discovery 1:100009. https://doi.org/10.59717/j.xinn-drugdisc.2026.100009
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Hu W., Sun Y., Li T., et al. (2026). The evolution of computation-driven paradigms in targeted peptide drug design: From predictive modeling to generative AI and clinical translation. The Innovation Drug Discovery 1:100009. https://doi.org/10.59717/j.xinn-drugdisc.2026.100009
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Figure 1.
Overview of Peptide Therapeutics
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Figure 2.
Integrated AI and physics-based workflow for targeted peptide drug design
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Figure 3.
Workflow illustrating the transition from traditional trial-and-error to rational peptide drug design
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Figure 4.
Classification of strategies and commonly used tools for peptide-protein molecular docking
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Figure 5.
AI-driven workflow for peptide discovery
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Figure 6.
Evolution of computational peptide drug design paradigms and the multi-level translational validation framework
