molfeat 0.11.0
molfeat - the hub for all your molecular featurizers
License: Apache
Requires Python: <3.11,>=3.9

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    <b>molfeat - the hub for all your molecular featurizers </b> <br />
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---

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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:

```bash
mamba install -c conda-forge molfeat
```

_**Tips:** You can replace `mamba` by `conda`._

_**Note:** We highly recommend using a [Conda Python distribution](https://github.com/conda-forge/miniforge) 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](https://github.com/datamol-io/molfeat/blob/main/pyproject.toml#L60) 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](docs/developers/create-plugin.md) 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

```python
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: [![DOI](https://zenodo.org/badge/613548667.svg)](https://zenodo.org/badge/latestdoi/613548667).

## Contribute

See [developers](docs/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](https://datamol.io/) ecosystem, please see [community](docs/community/).

## Maintainers

- @cwognum
- @maclandrol
- @hadim

## License

Under the Apache-2.0 license. See [LICENSE](LICENSE).