aceA Retrospective Benchmark of Quantitative Antimicrobial Peptide Activity Prediction Tools
Antimicrobial peptides (AMPs) are critical candidates for addressing the global antibiotic resistance crisis. AI-driven 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-world, out-of-the-box performance, we conducted a retrospective benchmark of 10 publicly released quantitative AMP prediction tools spanning cl…
Selected tools kept useful relative-ranking and virtual-screening enrichment ability, so they can work as early-stage filters when screening peptide candidates.
Most publicly released tools gave poor absolute MIC predictions and transferred badly to an external dataset, so they cannot replace experimental measurement.
The tools were assessed retrospectively, and most showed limited transferability to the external benchmark, so their predictions cannot stand in for experimental MIC measurements.
Evidence
- Peer-reviewedSmall Methods2026-10-15
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Truvace Impact Record TRV-2026-1949, v1: “A Retrospective Benchmark of Quantitative Antimicrobial Peptide Activity Prediction Tools.” Truvace, 2026-10-19. /record/TRV-2026-1949 (accessed at citation time). sha256 67248c584d42c029…
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