ArticleNature communications2022
AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers, 1 of them a synthesis that pooled it.
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96 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Proteomic profiling of bone for the estimation of post-mortem interval and post-mortem submersion interval: a systematic review.International journal of legal medicine · 2026Pooled it
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- MassNet: billion-scale AI-ready mass spectral corpus empowering scalable deep learning in proteomics.Nature methods · 2026Article
- Instrument-Software Synergy in Proteomics: Systematic Evaluation across Mass Spectrometry Platforms, Search Engines, and Rescoring Methods.Journal of proteome research · 2026Article
- Targeted quantification assays for DNA repair and handling proteins and interactions in Huntington's disease models.bioRxiv : the preprint server for biology · 2026Article
- InstaNovo-P: a de novo peptide sequencing model for phosphoproteomics.Nature communications · 2026Article
- DeepACPred: an integrated multistage framework for anticancer peptide discovery and activity prediction.Briefings in bioinformatics · 2026Article
- Cross-domain transfer learning from peptides to metabolites using a multi-property fine-tuned LLM.Bioinformatics (Oxford, England) · 2026Article
- AlphaDIA enables DIA transfer learning for feature-free proteomics.Nature biotechnology · 2026Article
- Advancing proteomic discovery through optimized multi-stage scoring and deep learning-enhanced open search.Bioinformatics (Oxford, England) · 2026Article
- iDeepLC: Chemical Structure Information Yields Improved Retention Time Prediction of Peptides with Unseen Modifications.Analytical chemistry · 2026Article
- Revisiting resonance-excitation collision-induced dissociation for data-independent acquisition.bioRxiv : the preprint server for biology · 2026Article
- Prioritizing peptides for targeted mass spectrometry experiments using deep learning.bioRxiv : the preprint server for biology · 2026Article
- Host cell protein impurities in therapeutic proteins: overview of advances in detection, nonconventional removal technologies and immunogenicity assessment.Journal of biological engineering · 2026Review
- A Framework for Database Search with AI Models in Mass Spectrometry-Based Proteomics.Journal of proteome research · 2026Review
- DDA-BERT: end-to-end training for data-dependent acquisition mass spectrometry-based proteomics.Nature communications · 2026Article
- Full-DIA enables complete single-cell proteomics from diaPASEF using deep learning.Genome biology · 2026Article
- Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS.Nature biotechnology · 2026Article
- Integration of alternative fragmentation techniques into standard LC-MS workflows using a single deep learning model enhances proteome coverage.Nature methods · 2026Article
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36 more citing papers are in PubMed but not listed here.
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9 authors.
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Abstract
Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid sequence with good accuracy. However, DL is a very rapidly developing field with new neural network architectures frequently appearing, which are challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, a modular Python framework built on the PyTorch DL library that learns and predicts the properties of peptides ( https://github.com/MannLabs/alphapeptdeep ). It features a model shop that enables non-specialists to create models in just a few lines of code. AlphaPeptDeep represents post-translational modifications in a generic manner, even if only the chemical composition is known. Extensive use of transfer learning obviates the need for large data sets to refine models for particular experimental conditions. The AlphaPeptDeep models for predicting retention time, collisional cross sections and fragment intensities are at least on par with existing tools. Additional sequence-based properties can also be predicted by AlphaPeptDeep, as demonstrated with a HLA peptide prediction model to improve HLA peptide identification for data-independent acquisition ( https://github.com/MannLabs/PeptDeep-HLA ).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.