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ReDeeM: Repository for Deep Learning Models for Mass Spectrometry

ReDeeM is a Rust crate designed for implementing deep learning models specifically tailored for mass spectrometry data. The primary goal of this project is to facilitate the prediction of peptide properties and to develop classifier scoring models (TDA).

Current Crates

The ReDeeM project consists of two primary crates:

1. redeem-properties:

   • This crate focuses on deep learning models for peptide property prediction. It implements models for predicting retention time (RT), ion mobility (IM), and MS2 fragment intensities using the Candle library.

   • Current Models

   Model	Name	Architecture	Implemented
   AlphaPept RT Model	redeem_properties::RTCNNLSTMModel	CNN-LSTM	✔️
   AlphaPept MS2 Model	redeem_properties::MS2BertModel	Bert	✔️
   AlphaPept IM Model	redeem_properties::CCSCNNLSTMModel	CNN-LSTM	✔️

2. redeem-classifiers:

   • This crate is aimed at developing semi-supervised scoring classifier models. The goal is to create models for separating target peptides from decoys.

   • Current Models

   Model	Name	Architecture	Implemented
   XGBoost Classifier	redeem_classifiers::XGBoostClassifier	XGBoost	✔️