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Pharmaceutics
Volume 18
Issue 5
10.3390/pharmaceutics18050537
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Review

Integrative Peptide Drug Development: Chemical Engineering, AI-Driven Design, and Cell-Penetrating Peptides

by
Yong Eun Jang
1,†,
Minjun Kwon
2,†,
Chan Woo Kwon
3,
Seok Gi Kim
2,
Ji Su Hwang
1,
Nimisha Pradeep George
4,
Seung Ryong Paik
2,5,
Sampa Misra
2,
Shaherin Basith
2,
Seung Soo Sheen
6,* and
Gwang Lee
2,4,*
1
Molecular Science and Technology Research Center, Ajou University, Suwon 16499, Republic of Korea
2
Department of Physiology, Ajou University School of Medicine, Suwon 16499, Republic of Korea
3
Ajou University School of Medicine, Suwon 16499, Republic of Korea
4
Department of Molecular Science and Technology, Ajou University, Suwon 16499, Republic of Korea
5
Department of Chemistry, Kyonggi University, Suwon 16227, Republic of Korea
6
Department of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Pharmaceutics 2026, 18(5), 537; https://doi.org/10.3390/pharmaceutics18050537
Submission received: 13 March 2026 / Revised: 20 April 2026 / Accepted: 23 April 2026 / Published: 28 April 2026
(This article belongs to the Special Issue Biomedical Applications: Advances in Bioengineering and Drug Delivery, 2nd Edition)
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Abstract

Peptide therapeutics occupy a unique chemical space between small molecules and biologics, combining high target specificity with structural programmability and favorable safety profiles. Recent regulatory approvals and expanding clinical pipelines underscore the growing therapeutic and commercial relevance of peptide-based drugs. This review outlines chemical modification approaches and contemporary design strategies, and evaluates their impact on proteolytic stability, pharmacokinetics, membrane permeability, and target engagement. We then highlight recent advances in artificial intelligence (AI)-guided peptide drug design, including machine learning models, protein language models, and generative architectures that enable high-throughput activity prediction, property optimization, and de novo sequence generation. These approaches collectively accelerate the traditional discovery–design–validation cycle while reducing experimental attrition through data-driven, structure-informed modeling frameworks. Among these applications, AI also enables the rational design of cell-penetrating peptides (CPPs) to enhance intracellular delivery and biological activity. Building on these methodological advances, we further examine their application to peptide therapeutics, with particular emphasis on AI-based predictive models for CPPs as well as on therapeutic applications within the central nervous and pulmonary systems. We conclude by outlining future perspectives and emphasize that the systematic integration of AI-enabled sequence design with rational chemical engineering and advanced delivery technologies, supported by rigorous experimental validation, will be critical for developing robust and clinically durable peptide-based medicines.
Keywords: peptide therapeutics; cell-penetrating peptide; AI drug design; deep learning; machine learning peptide therapeutics; cell-penetrating peptide; AI drug design; deep learning; machine learning

Share and Cite

MDPI and ACS Style

Jang, Y.E.; Kwon, M.; Kwon, C.W.; Kim, S.G.; Hwang, J.S.; George, N.P.; Paik, S.R.; Misra, S.; Basith, S.; Sheen, S.S.; et al. Integrative Peptide Drug Development: Chemical Engineering, AI-Driven Design, and Cell-Penetrating Peptides. Pharmaceutics 2026, 18, 537. https://doi.org/10.3390/pharmaceutics18050537

AMA Style

Jang YE, Kwon M, Kwon CW, Kim SG, Hwang JS, George NP, Paik SR, Misra S, Basith S, Sheen SS, et al. Integrative Peptide Drug Development: Chemical Engineering, AI-Driven Design, and Cell-Penetrating Peptides. Pharmaceutics. 2026; 18(5):537. https://doi.org/10.3390/pharmaceutics18050537

Chicago/Turabian Style

Jang, Yong Eun, Minjun Kwon, Chan Woo Kwon, Seok Gi Kim, Ji Su Hwang, Nimisha Pradeep George, Seung Ryong Paik, Sampa Misra, Shaherin Basith, Seung Soo Sheen, and et al. 2026. "Integrative Peptide Drug Development: Chemical Engineering, AI-Driven Design, and Cell-Penetrating Peptides" Pharmaceutics 18, no. 5: 537. https://doi.org/10.3390/pharmaceutics18050537

APA Style

Jang, Y. E., Kwon, M., Kwon, C. W., Kim, S. G., Hwang, J. S., George, N. P., Paik, S. R., Misra, S., Basith, S., Sheen, S. S., & Lee, G. (2026). Integrative Peptide Drug Development: Chemical Engineering, AI-Driven Design, and Cell-Penetrating Peptides. Pharmaceutics, 18(5), 537. https://doi.org/10.3390/pharmaceutics18050537

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