{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T17:13:12Z","timestamp":1783617192394,"version":"3.55.0"},"reference-count":124,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pharmaceuticals"],"abstract":"<jats:p>The majority of disease-associated proteins are considered \u201cundruggable\u201d due to the absence of well-defined binding pockets, the presence of extended interaction surfaces, and intrinsic structural disorder, which collectively limit the effectiveness of conventional small molecules and biologics. Representative examples include KRAS, p53, and c-MYC. Peptide therapeutics, particularly macrocyclic peptides, occupy a unique chemical space capable of targeting such recalcitrant protein\u2013protein interactions (PPIs) where small molecules often fail. However, traditional peptide discovery, which relies heavily on high-throughput screening, is labor-intensive and frequently yields candidates with suboptimal pharmacological properties. The integration of artificial intelligence has begun to transform peptide discovery from a largely empirical process into a rational and design-driven paradigm. Modern deep learning approaches, including diffusion-based generative models, enable the de novo design of peptide binders with high affinity and structural precision, even for disordered or previously intractable targets. In this perspective, we highlight key structural and biological challenges associated with undruggable proteins and consider how peptide-based modalities are beginning to overcome these longstanding barriers. We further explore how advances in artificial intelligence and computational modeling may reshape the rational design of next-generation peptide therapeutics and propose an integrated experimental\u2013computational framework to facilitate the development of clinically actionable candidates.<\/jats:p>","DOI":"10.3390\/ph19070998","type":"journal-article","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T15:52:24Z","timestamp":1783007544000},"page":"998","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Targeting the Undruggable: Deep Learning-Driven Design of Peptide Therapeutics in Cancer"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3511-1835","authenticated-orcid":false,"given":"Ha Thi Ngoc","family":"Nguyen","sequence":"first","affiliation":[{"name":"Faculty of Pharmacy, Lac Hong University, Dong Nai 810000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bao Hong Ngoc","family":"Le","sequence":"additional","affiliation":[{"name":"College of Pharmacy, Dongguk University, Seoul 04620, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7738-8777","authenticated-orcid":false,"given":"Nhung Thi Hong","family":"Van","sequence":"additional","affiliation":[{"name":"Department of Physiology, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Trinh Thi Tuyet","family":"Tran","sequence":"additional","affiliation":[{"name":"Vinmec-VinUni Institute of Immunology, Vin University, Hanoi 100000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6215-8266","authenticated-orcid":false,"given":"Minh Tuan","family":"Nguyen","sequence":"additional","affiliation":[{"name":"College of Pharmacy, Dongguk University, Seoul 04620, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1038\/s41392-023-01589-z","article-title":"Recent Advances in Targeting the \u201cUndruggable\u201d Proteins: From Drug Discovery to Clinical Trials","volume":"8","author":"Xie","year":"2023","journal-title":"Signal Transduct. 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