{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T04:49:46Z","timestamp":1793335786375,"version":"4.5.0"},"reference-count":125,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Mol. Biosci."],"abstract":"<jats:p>\n                    Bioactive peptides have emerged as a promising therapeutic class for dyslipidemia and residual cardiovascular risk, operating through mechanisms that include apolipoprotein mimicry, PCSK9 inhibition, and modulation of cholesterol absorption and inflammation, yet translational success remains constrained by poor pharmacokinetics, limited oral bioavailability, and inconsistent clinical outcomes. Computational approaches, ranging from machine learning classifiers and protein language models to structure-guided docking and generative design, now accelerate peptide discovery by enabling high-throughput screening, rational optimization of stability and affinity, and\n                    <jats:italic>de novo<\/jats:italic>\n                    generation of function-tailored sequences. However, the field faces persistent bottlenecks: lipid-annotated peptide datasets are scarce and fragmented, negative sampling biases inflate model performance, and most predictive algorithms trained on antimicrobial or general bioactive peptide data generalize poorly to cardiovascular applications, with many computationally derived leads lacking orthogonal peptidomics or proteolytic validation and consequently yielding high false-positive rates. Recent successes, particularly the development of orally bioavailable macrocyclic PCSK9 inhibitors, demonstrate that integrated pipelines combining structure-based design, display technologies, and multi-parameter optimization can overcome these barriers. Moving forward, progress will require concerted efforts to build high-quality, standardized lipid-peptide repositories, adopt uncertainty-aware and domain-adaptive modeling strategies, and embed early-stage developability filters into computational workflows. The convergence of generative artificial intelligence, population genomics, and precision medicine may ultimately enable patient-tailored peptide therapeutics capable of addressing the heterogeneous nature of dyslipidemia and atherosclerotic disease.\n                  <\/jats:p>","DOI":"10.3389\/fmolb.2026.1818173","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T04:48:01Z","timestamp":1779338881000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Bioactive peptides targeting dyslipidemia and atherosclerosis: from computational discovery and data bottlenecks to precision medicine translation"],"prefix":"10.3389","volume":"13","author":[{"given":"Wei","family":"Yin","sequence":"first","affiliation":[{"name":"Guangxi International Zhuang Medicine Hospital Affiliated to Guangxi University of Chinese Medicine","place":["Nanning, Guangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Guangxi International Zhuang Medicine Hospital Affiliated to Guangxi University of Chinese Medicine","place":["Nanning, Guangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ZhiMing","family":"Zeng","sequence":"additional","affiliation":[{"name":"The Reproductive Hospital of Guangxi Zhuang Autonoumous Region","place":["Nanning, Guangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Xie","sequence":"additional","affiliation":[{"name":"Guangxi International Zhuang Medicine Hospital Affiliated to Guangxi University of Chinese Medicine","place":["Nanning, Guangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,21]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1038\/s42003-022-03445-2","article-title":"PepNN: a deep attention model for the identification of peptide binding sites","volume":"5","author":"Abdin","year":"2022","journal-title":"Commun. 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