ms2deepscore 0.2.0

Deep learning similarity measure for comparing MS/MS spectra.

ms2deepscore

ms2deepscore provides a Siamese neural network that is trained to predict molecular structural similarities (Tanimoto scores) from pairs of mass spectrometry spectra.

The library provides an intuitive classes to prepare data, train a siamese model, and compute similarities between pairs of spectra.

In addition to the prediction of a structural similarity, MS2DeepScore can also make use of Monte-Carlo dropout to assess the model uncertainty.

Setup

Requirements

Python 3.7 or higher

Installation

Simply install using pip: pip install ms2deepscore

Prepare environment

We recommend to create an Anaconda environment with

conda create --name ms2deepscore python=3.8
conda activate ms2deepscore
pip install ms2deepscore

Alternatively, simply install in the environment of your choice by .

Or, to also include the full matchms functionality:

conda create --name ms2deepscore python=3.8
conda activate ms2deepscore
conda install --channel bioconda --channel conda-forge matchms
pip install ms2deepscore

Quick start: How to prepare data, train a model, and compute similarities.

See notebooks/MS2DeepScore_tutorial.ipynb for a more extensive fully-working example on test data.

Data preperation

Bin spectrums using ms2deepscore.SpectrumBinner. In this binned form we can feed spectra to the model.

from ms2deepscore import SpectrumBinner
spectrum_binner = SpectrumBinner(1000, mz_min=10.0, mz_max=1000.0, peak_scaling=0.5)
binned_spectrums = spectrum_binner.fit_transform(spectrums)

Create a data generator that will generate batches of training examples. Each training example consists of a pair of binned spectra and the corresponding reference similarity score.

from ms2deepscore.data_generators import DataGeneratorAllSpectrums
dimension = len(spectrum_binner.known_bins)
data_generator = DataGeneratorAllSpectrums(binned_spectrums, tanimoto_scores_df,
                                           dim=dimension)

Train a model

Initialize and train a SiameseModel. It consists of a dense 'base' network that produces an embedding for each of the 2 inputs. The 'head' model computes the cosine similarity between the embeddings.

from tensorflow import keras
from ms2deepscore.models import SiameseModel
model = SiameseModel(spectrum_binner, base_dims=(200, 200, 200), embedding_dim=200,
                     dropout_rate=0.2)
model.compile(loss='mse', optimizer=keras.optimizers.Adam(lr=0.001))
model.fit(data_generator,
          validation_data=data_generator,
          epochs=2)

Predict similarity scores

Calculate similariteis for a pair of spectra

from ms2deepscore import MS2DeepScore
similarity_measure = MS2DeepScore(model)
score = similarity_measure.pair(spectrums[0], spectrums[1])

Contributing

We welcome contributions to the development of ms2deepscore! Have a look at the contribution guidelines.