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AI\u2010driven quantitative models capable of directly predicting peptide antimicrobial activity, specifically minimum inhibitory concentration (MIC), are gaining scientific prominence by providing precise guidance for the rational design of AMPs. To assess their real\u2010world, out\u2010of\u2010the\u2010box performance, we conducted a retrospective benchmark of 10 publicly released quantitative AMP prediction tools spanning classical machine learning and contemporary deep learning approaches. Using an external dataset and reconstructed native datasets, we evaluated absolute MIC regression, retrospective reproducibility, directional prediction patterns, ranking capability, and virtual\u2010screening enrichment. Most released tools exhibited poor absolute MIC regression and limited transferability to the external benchmark, while selected tools retained useful relative\u2010ranking and enrichment capabilities. Retrospective evaluation further revealed marked differences in implementation\u2010level reproducibility among the released tools. Error\u2010stratified analyses identified systematic associations between prediction errors and peptide residue composition, together with a general compression of the predicted activity range. Collectively, these findings characterize the practical performance boundaries of currently accessible quantitative AMP prediction tools and support their use primarily as early\u2010stage screening filters rather than precise substitutes for experimental MIC measurements.<\/jats:p>","DOI":"10.1002\/smtd.71137","type":"journal-article","created":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T06:55:32Z","timestamp":1792133732000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Retrospective Benchmark of Quantitative Antimicrobial Peptide Activity Prediction Tools"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7422-8988","authenticated-orcid":false,"given":"Jianda","family":"Yue","sequence":"first","affiliation":[{"name":"The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development College of Life Sciences Hunan Normal University  Changsha 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