recipe ms2deepscore

Deep learning similarity measure for comparing MS/MS spectra with respect to their chemical similarity

Homepage:

https://github.com/matchms/ms2deepscore

License:

APACHE / Apache-2.0

Recipe:

/ms2deepscore/meta.yaml

Links:

doi: 10.1186/s13321-021-00558-4, biotools: ms2deepscore

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

package ms2deepscore

versions:

2.5.0-0,  2.4.0-0,  2.3.0-0,  2.2.0-0,  2.1.0-0,  2.0.0-0,  1.0.0-0,  0.5.0-0,  0.4.0-0, 

2.5.0-0,  2.4.0-0,  2.3.0-0,  2.2.0-0,  2.1.0-0,  2.0.0-0,  1.0.0-0,  0.5.0-0,  0.4.0-0,  0.3.0.1-0

depends matchms:

>=0.18.0

depends matplotlib-base:

3.7.2

depends numba:

depends numpy:

>=1.20.3

depends pandas:

depends python:

>=3.9

depends pytorch:

depends scikit-learn:

depends tensorboard:

depends torchvision:

depends tqdm:

requirements:

additional platforms:

Installation

You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install ms2deepscore

and update with::

   mamba update ms2deepscore

To create a new environment, run:

mamba create --name myenvname ms2deepscore

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull quay.io/biocontainers/ms2deepscore:<tag>

(see `ms2deepscore/tags`_ for valid values for ``<tag>``)

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