datamol-io/molfeat - Githubissues

molfeat - the hub for all your molecular featurizers

Molfeat is a hub of molecular featurizers. It supports a wide variety of out-of-the-box molecular featurizers and can be easily extended to include your own custom featurizers.

🚀 Fast, with a simple and efficient API.
🔄 Unify pre-trained molecular embeddings and hand-crafted featurizers in a single package.
➕ Easily add your own featurizers through plugins.
📈 Benefit from increased performance through a trouble-free caching system.

Visit our website at https://molfeat.datamol.io.

Installation

Installing Molfeat

Use mamba:

mamba install -c conda-forge molfeat

Tips: You can replace mamba by conda.

Note: We highly recommend using a Conda Python distribution to install Molfeat. The package is also pip installable if you need it: pip install molfeat.

Optional dependencies

Not all featurizers in the Molfeat core package are supported by default. Some featurizers require additional dependencies. If you try to use a featurizer that requires additional dependencies, Molfeat will raise an error and tell you which dependencies are missing and how to install them.

To install dgl: run mamba install -c dglteam "dgl<=2.0" # there is some issue with "dgl>2.0.0" related to graphbolt
To install dgllife: run mamba install -c conda-forge dgllife
To install fcd_torch: run mamba install -c conda-forge fcd_torch
To install pyg: run mamba install -c conda-forge pytorch_geometric
To install graphormer-pretrained: run mamba install -c conda-forge graphormer-pretrained
To install map4: see https://github.com/reymond-group/map4
To install bio-embeddings: run mamba install -c conda-forge 'bio-embeddings >=0.2.2'

If you install Molfeat using pip, there are optional dependencies that can be installed with the main package. For example, pip install "molfeat[all]" allows installing all the compatible optional dependencies for small molecule featurization. There are other options such as molfeat[dgl], molfeat[graphormer], molfeat[transformer], molfeat[viz], and molfeat[fcd]. See the optional-dependencies for more information.

Installing Plugins

The functionality of Molfeat can be extended through plugins. The use of a plugin system ensures that the core package remains easy to install and as light as possible, while making it easy to extend its functionality with plug-and-play components. Additionally, it ensures that plugins can be developed independently from the core package, removing the bottleneck of a central party that reviews and approves new plugins. Consult the molfeat documentation for more details on how to create your own plugins.

However, this does imply that the installation of a plugin is plugin-dependent: please consult the relevant documentation to learn more.

API tour

import datamol as dm
from molfeat.calc import FPCalculator
from molfeat.trans import MoleculeTransformer
from molfeat.store.modelstore import ModelStore

# Load some dummy data
data = dm.data.freesolv().sample(100).smiles.values

# Featurize a single molecule
calc = FPCalculator("ecfp")
calc(data[0])

# Define a parallelized featurization pipeline
mol_transf = MoleculeTransformer(calc, n_jobs=-1)
mol_transf(data)

# Easily save and load featurizers
mol_transf.to_state_yaml_file("state_dict.yml")
mol_transf = MoleculeTransformer.from_state_yaml_file("state_dict.yml")
mol_transf(data)

# List all available featurizers
store = ModelStore()
store.available_models

# Find a featurizer and learn how to use it
model_card = store.search(name="ChemBERTa-77M-MLM")[0]
model_card.usage()

How to cite

Please cite Molfeat if you use it in your research: .

Contribute

See developers for a comprehensive guide on how to contribute to molfeat. molfeat is a community-led initiative and whether you're a first-time contributor or an open-source veteran, this project greatly benefits from your contributions. To learn more about the community and datamol.io ecosystem, please see community.

Maintainers

@cwognum
@maclandrol
@hadim

License

Under the Apache-2.0 license. See LICENSE.