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Meet FIORA! An in silico fragmentation algorithm designed to predict tandem mass spectra (MS/MS) with high accuracy. Using graph neural networks, FIORA models bond cleavages, fragment intensities, and estimates retention times (RT) and collision cross sections (CCS).

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# **FIORA**

Meet FIORA! An in silico fragmentation algorithm designed to predict tandem mass spectra (MS/MS) with high accuracy. Using a graph neural network, FIORA models bond cleavages, fragment intensities, and estimates retention times (RT) and collision cross sections (CCS). Also available on our [online prediction platform](https://apps.bam.de/shn01/fioRa/).

> Nowatzky, Y., Russo, F.F., Lisec, J. et al. FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events. Nat Commun 16, 2298 (2025). https://doi.org/10.1038/s41467-025-57422-4

<br>

<figure>
  <img src="images/graphical_abstract.png" alt="Graphical Abstract" style="width:100%;">
  <figcaption>Fig. 1 | Illustration of the in silico fragmentation workflow (bottom panel) designed to simulate experimental MS/MS fragmentation (top panel).
   The figure is sourced from our publication (referenced above) and is licensed under <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">CC BY 4.0</a>.</figcaption>
</figure>

## Requirements

Developed and tested with the following systems and versions:
* Debian GNU/Linux 11 (bullseye)
* Python 3.10.8
* GCC 11.2.0

## Installation

Installation guide for the FIORA Python package (under 10 minutes):

Clone the project folder

git clone https://github.com/BAMeScience/fiora.git

(Optional) Create a new conda environment

conda create -n fiora python=3.10.8
    conda activate fiora

Change into the project directory (`cd fiora`). Then, install the package (optionally using constraints.txt)

pip install . -c constraints.txt

(Optional) You may want to test that the package works as intended. This can be done by running the sripts in the *tests* directory or by using pytest (requires: `pip install pytest`)

pytest -v tests

(Optional) Reproduce complete environment FIORA was developed with:

pip install -r requirements.txt -c constraints.txt

## Usage

### MS/MS prediction

Use spectral prediction function as follows:

fiora-predict [-h] -i INPUT -o OUTPUT [--model MODEL] [--rt | --no-rt] [--ccs | --no-ccs] [--annotation | --no-annotation]

An input csv file must be provided and an output file specified (`mgf` or `msp` format).

### Input format

Input files are expected to be in csv format. With a header defining the columns: "Name", "SMILES", "Precursor_type", "CE", "Instrument_type" and rows listing individual queries.
See example [input file](examples/example_input.csv).

### Output format

Predicted spectra are provided in standard `msp` and `mgf` format.

### Example usage

Run the fiora-predict from within this directory

fiora-predict -i examples/example_input.csv  -o examples/example_spec.mgf

By default, an open-source model is selected automatically, and predictions typically complete within a few seconds. For faster performance, specify a GPU device using the `--dev` option (e.g., `--dev cuda:0`). The output file (e.g., examples/example_spec.mgf) can be compared with the [expected results](examples/expected_output.mgf) to verify model accuracy. This verification is automatically performed by running pytest (as described above).

### Models and Resources

Default models are packaged under `fiora/resources/models`. The CLI uses these automatically when `--model default` is selected.

Scripts for downloading and preprocessing MSnLib are provided in `resources/data/msnlib` (`download_msnlib.py` and `preprocess_msnlib.py`).

The downloader defaults to MSnLib v7 on Zenodo (`https://zenodo.org/records/16984129`) and accepts both direct file URLs and Zenodo record URLs. For Zenodo record URLs it downloads all files matching `*_ms2.mgf` by default.

```bash
python resources/data/msnlib/download_msnlib.py
python resources/data/msnlib/preprocess_msnlib.py
```

Use `--record-pattern` to select a different subset, e.g. `--record-pattern "*_pos_*.mgf"`.

### MSnLib (Re)training

To (re)train a new FIORA model, use `fiora-train`. For example, to train on MSnLib with the same parameters used for the v1.0 release:

```bash
fiora-train \
  -i resources/data/msnlib/library.csv \
  -o checkpoints/fiora.pt \
  --device cuda:0 \
  --instruments HCD \
  --precursor-modes "[M+H]+,[M-H]-,[M]+,[M]-"
```

To persist per-epoch training history, add `--history-out` (supports `.json` or `.csv`):

```bash
fiora-train ... --history-out checkpoints/fiora_history.json
```

### Model Evaluation CLI

You can evaluate a trained model on validation/test splits with:

```bash
fiora-eval \
  -i resources/data/msnlib/library.csv \
  -m checkpoints/fiora.pt \
  --device cuda:0 \
  --output-dir checkpoints/eval
```

This prints split-level summary scores (default: `spectral_sqrt_cosine`) and, when available, also reports precursor-excluded metrics (`spectral_sqrt_cosine_wo_prec`, `spectral_sqrt_cosine_avg`). Per-split result files like `validation_eval.csv` and `test_eval.csv` are written when `--output-dir` is set.

### Model Info CLI

To inspect key parameters of a trained model checkpoint:

```bash
fiora-model-info -m checkpoints/fiora.pt
```

Use `--as-json` to print the full `model_params` dictionary.

## The Algorithm

FIORA has been developed as a computational tool to predict bond cleavages that occur in the MS/MS fragmentation process and estimate the probabilities of resulting fragment ions. To that end, FIORA utilizes graph neural networks to learn local molecular neighborhoods around bonds, combined with edge prediction to simulate bond dissociation. The prediction determines which fragment (left or right of the bond cleavage, with up to four possible hydrogen losses) retains the charge and which becomes the neutral loss. The figure below illustrates an example fragmentation prediction for a single bond.

<figure>
  <img src="images/fragmentation_algorithm.png" alt="Fragmentation Algorithm" style="width:100%;">
  <figcaption>Fig. 2 | Illustration of FIORA's fragmentation algorithm.  Initially, the local neighborhood (highlighted in blue) of the designated bond is learned through multiple graph convolutions (two shown here). Based on this molecular substructure and bond features, fragment abundances are predicted. In this example, FIORA predicts the loss of two hydrogen atoms, indicating the formation of a new double bond in the right fragment. 
  The figure is sourced from our publication (referenced on top) and is licensed under <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">CC BY 4.0</a>.</figcaption>
</figure>

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