Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models - Angelis - 2025 - PROTEOMICS - Wiley Online Library

Funding: This work was in part funded by the German Federal Ministry of Education and Research (031L0305A) and an European Research Council Starting Grant (101077037). Give access Share full-text access Use the link below to share a full-text version of this article with your friends and colleagues. Learn more. Share a link This review explores state of the art machine learning and deep learning models for peptide property prediction in mass spectrometry-based proteomics, including, but not limited to, models for predicting digestibility, retention time, charge state distribution, collisional cross section, fragmentation ion intensities, and detectability. The combination of these models enables not only the in silico generation of spectral libraries but also finds many additional use cases in the design of targeted assays or data-driven rescoring. This review serves as both an introduction for newcomers and an update for experienced researchers aiming to develop accessible and reprodu

Funding: This work was in part funded by the German Federal Ministry of Education and Research (031L0305A) and an European Research Council Starting Grant (101077037). Give access Share full-text access Use the link below to share a full-text version of this article with your friends and colleagues. Learn more. Share a link This review explores state of the art machine learning and deep learning models for peptide property prediction in mass spectrometry-based proteomics, including, but not limited to, models for predicting digestibility, retention time, charge state distribution, collisional