biostudies-literatureAbdul-Khalek NElitenetzwerk BayernBundesministerium f?r Bildung und ForschungEuropean Research CouncilKarl Pedersens og Hustrus Industrifond2709-2726https://www.ebi.ac.uk/biostudies/studies/S-EPMC12150320biostudies-literatureUnknown24(6)Identifying detectable peptides, known as flyers, is key in mass spectrometry-based proteomics. Peptide detectability is strongly related to peptide sequences and their resulting physicochemical properties. Moreover, the high variability in MS data challenges the development of a generic model for detectability prediction, underlining the need for customizable tools. We present Pfly, a deep learning model developed to predict peptide detectability based solely on peptide sequence. Pfly is a versatile and reliable state-of-the-art tool, offering high performance, accessibility, and easy customizability for end-users. This adaptability allows researchers to tailor Pfly to specific experimental conditions, improving accuracy and expanding applicability across various research fields. Pfly is Journal of proteome researchTo Fly, or Not to Fly, That Is the Question: A Deep Learning Model for Peptide Detectability Prediction in Mass Spectrometry.PMC12150320DI-201907020031L0305AF-6-M5613.6.K-NW-2021-411/1/1101077037Picciani MAbdul-Khalek NWimmer RShouman OOvergaard MTWilhelm MGregersen Echers SfalseTo Fly, or Not to Fly, That Is the Question: A Deep Learning Model for Peptide Detectability Prediction in Mass Spectrometry.Identifying detectable peptides, known as flyers, is key in mass spectrometry-based proteomics. Peptide detectability is strongly related to peptide sequences and their resulting physicochemical properties. Moreover, the high variability in MS data challenges the development of a generic model for detectability prediction, underlining the need for customizable tools. We present Pfly, a deep learning model developed to predict peptide detectability based solely on peptide sequence. Pfly is a versatile and reliable state-of-the-art tool, offering high performance, accessibility, and easy customizability for end-users. This adaptability allows researchers to tailor Pfly to specific experimental conditions, improving accuracy and expanding applicability across various research fields. Pfly is 2025-01-01T00:00:00Z2025 Jun2026-06-02T02:34:47.159Z2026-05-25T03:07:02.851ZS-EPMC121503204034420110.1021/acs.jproteome.4c00973