recipe deepfplearn

Link molecular structures of chemicals (in form of topological fingerprints) with multiple targets.

Homepage:

https://github.com/yigbt/deepFPlearn

License:

GPL / GPL-3.0-or-later

Recipe:

/deepfplearn/meta.yaml

package deepfplearn

versions:

2.0-0,  1.2-0

depends chemprop:

depends descriptastorus:

depends flask:

>=1.1.2

depends hyperopt:

>=0.2.3

depends jsonpickle:

2.1.*

depends keras:

2.9.*

depends matplotlib-base:

3.5.1.*

depends numpy:

1.22.*

depends pandas:

1.4.*

depends pandas-flavor:

>=0.2.0

depends python:

depends pytorch:

>=1.5.1

depends rdkit:

2022.03.*

depends scikit-learn:

1.0.*

depends scipy:

>=1.4.1

depends seaborn:

0.12.2.*

depends sphinx:

>=3.1.2

depends tensorboardx:

>=2.0

depends tensorflow-base:

depends tqdm:

>=4.45.0

depends typed-argument-parser:

>=1.6.1

depends umap-learn:

0.5.3.*

depends wandb:

0.12.*

requirements:

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 deepfplearn

and update with::

   mamba update deepfplearn

To create a new environment, run:

mamba create --name myenvname deepfplearn

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/deepfplearn:<tag>

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

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