Metadata-Version: 2.1
Name: selfies
Version: 2.0.0
Summary: SELFIES (SELF-referencIng Embedded Strings) is a general-purpose, sequence-based, robust representation of semantically constrained graphs.
Home-page: https://github.com/aspuru-guzik-group/selfies
Author: Mario Krenn, Alston Lo, and many other contributors
Author-email: mario.krenn@utoronto.ca, alan@aspuru.com
License: UNKNOWN
Description: # SELFIES
        
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        **Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation**\
        _Mario Krenn, Florian Haese, AkshatKumar Nigam, Pascal Friederich, Alan Aspuru-Guzik_\
        [*Machine Learning: Science and Technology* **1**, 045024 (2020)](https://iopscience.iop.org/article/10.1088/2632-2153/aba947), [extensive blog post January 2021](https://aspuru.substack.com/p/molecular-graph-representations-and).\
        [Talk on youtube about SELFIES](https://www.youtube.com/watch?v=CaIyUmfGXDk).\
        [Blog explaining SELFIES in Japanese language](https://blacktanktop.hatenablog.com/entry/2021/08/12/115613)\
        Major contributors since v1.0.0: _[Alston Lo](https://github.com/alstonlo) and [Seyone Chithrananda](https://github.com/seyonechithrananda)_\
        Chemistry Advisor: [Robert Pollice](https://scholar.google.at/citations?user=JR2N3JIAAAAJ)
        
        ---
        
        A main objective is to use SELFIES as direct input into machine learning models,
        in particular in generative models, for the generation of molecular graphs
        which are syntactically and semantically valid.
        
        <p align="center">
           <img src="https://github.com/aspuru-guzik-group/selfies/blob/master/examples/VAE_LS_Validity.png" alt="SELFIES validity in a VAE latent space" width="666px">
        </p>
        
        ## Installation
        Use pip to install ``selfies``.
        
        ```bash
        pip install selfies
        ```
        
        To check if the correct version of ``selfies`` is installed, use
        the following pip command. 
        
        ```bash
        pip show selfies
        ```
        
        To upgrade to the latest release of ``selfies`` if you are using an 
        older version, use the following pip command. Please see the 
        [CHANGELOG](https://github.com/aspuru-guzik-group/selfies/blob/master/CHANGELOG.md) 
        to review the changes between versions of `selfies`, before upgrading: 
        
        ```bash
        pip install selfies --upgrade 
        ```
        
        
        ## Usage
        
        ### Overview
        
        Please refer to the [documentation](https://selfiesv2.readthedocs.io/en/latest/),
        which contains a thorough tutorial  for getting started with ``selfies`` 
        and detailed descriptions of the functions
        that ``selfies`` provides. We summarize some key functions below.
        
        | Function | Description |
        | -------- | ----------- |
        | ``selfies.encoder`` | Translates a SMILES string into its corresponding SELFIES string. |
        | ``selfies.decoder`` | Translates a SELFIES string into its corresponding SMILES string. |
        | ``selfies.set_semantic_constraints`` | Configures the semantic constraints that ``selfies`` operates on. |
        | ``selfies.len_selfies`` | Returns the number of symbols in a SELFIES string. |
        | ``selfies.split_selfies`` | Tokenizes a SELFIES string into its individual symbols. |
        | ``selfies.get_alphabet_from_selfies`` | Constructs an alphabet from an iterable of SELFIES strings. |
        | ``selfies.selfies_to_encoding`` | Converts a SELFIES string into its label and/or one-hot encoding. |
        | ``selfies.encoding_to_selfies`` | Converts a label or one-hot encoding into a SELFIES string. |
        
        
        ### Examples
        
        #### Translation between SELFIES and SMILES representations:
        
        ```python
        import selfies as sf
        
        benzene = "c1ccccc1"
        
        # SMILES -> SELFIES -> SMILES translation
        try:
            benzene_sf = sf.encoder(benzene)  # [C][=C][C][=C][C][=C][Ring1][=Branch1]
            benzene_smi = sf.decoder(benzene_sf)  # C1=CC=CC=C1
        except sf.EncoderError:
            pass  # sf.encoder error!
        except sf.DecoderError:
            pass  # sf.decoder error!
        
        len_benzene = sf.len_selfies(benzene_sf)  # 8
        
        symbols_benzene = list(sf.split_selfies(benzene_sf))
        # ['[C]', '[=C]', '[C]', '[=C]', '[C]', '[=C]', '[Ring1]', '[=Branch1]']
        ```
        
        #### Customizing SELFIES:
        
        In this example, we relax the semantic constraints of ``selfies`` to allow
        for hypervalences (caution: hypervalence rules are much less understood
        than octet rules. Some molecules containing hypervalences are important, 
        but generally, it is not known which molecules are stable and reasonable).
        
        ```python
        import selfies as sf
        
        hypervalent_sf = sf.encoder('O=I(O)(O)(O)(O)O', strict=False)  # orthoperiodic acid
        standard_derived_smi = sf.decoder(hypervalent_sf)
        # OI (the default constraints for I allows for only 1 bond)
        
        sf.set_semantic_constraints("hypervalent")
        relaxed_derived_smi = sf.decoder(hypervalent_sf)
        # O=I(O)(O)(O)(O)O (the hypervalent constraints for I allows for 7 bonds)
        ```
        
        #### Integer and one-hot encoding SELFIES:
        
        In this example, we first build an alphabet from a dataset of SELFIES strings, 
        and then convert a SELFIES string into its padded encoding. Note that we use the
        ``[nop]`` ([no operation](https://en.wikipedia.org/wiki/NOP_(code) ))
        symbol to pad our SELFIES, which is a special SELFIES symbol that is always
        ignored and skipped over by ``selfies.decoder``, making it a useful
        padding character.
        
        ```python
        import selfies as sf
        
        dataset = ["[C][O][C]", "[F][C][F]", "[O][=O]", "[C][C][O][C][C]"]
        alphabet = sf.get_alphabet_from_selfies(dataset)
        alphabet.add("[nop]")  # [nop] is a special padding symbol
        alphabet = list(sorted(alphabet))  # ['[=O]', '[C]', '[F]', '[O]', '[nop]']
        
        pad_to_len = max(sf.len_selfies(s) for s in dataset)  # 5
        symbol_to_idx = {s: i for i, s in enumerate(alphabet)}
        
        dimethyl_ether = dataset[0]  # [C][O][C]
        
        label, one_hot = sf.selfies_to_encoding(
           selfies=dimethyl_ether,
           vocab_stoi=symbol_to_idx,
           pad_to_len=pad_to_len,
           enc_type="both"
        )
        # label = [1, 3, 1, 4, 4]
        # one_hot = [[0, 1, 0, 0, 0], [0, 0, 0, 1, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 1], [0, 0, 0, 0, 1]]
        ```
        
        ### More Usages and Examples
        
        * More examples can be found in the ``examples/`` directory, including a
        [variational autoencoder that runs on the SELFIES](https://github.com/aspuru-guzik-group/selfies/tree/master/examples/vae_example) language.
        * This [ICLR2020 paper](https://arxiv.org/abs/1909.11655) used SELFIES in a
        genetic algorithm to achieve state-of-the-art performance for inverse design,
        with the [code here](https://github.com/aspuru-guzik-group/GA).
        * SELFIES allows for [highly efficient exploration and interpolation of the chemical space](https://chemrxiv.org/articles/preprint/Beyond_Generative_Models_Superfast_Traversal_Optimization_Novelty_Exploration_and_Discovery_STONED_Algorithm_for_Molecules_using_SELFIES/13383266), with a [deterministic algorithms, see code](https://github.com/aspuru-guzik-group/stoned-selfies).
        * We use SELFIES for [Deep Molecular dreaming](https://arxiv.org/abs/2012.09712), a new generative model inspired by interpretable neural networks in computational vision. See the [code of PASITHEA here](https://github.com/aspuru-guzik-group/Pasithea).
        * Kohulan Rajan, Achim Zielesny, Christoph Steinbeck show in two papers that SELFIES outperforms other representations in [img2string](https://link.springer.com/article/10.1186/s13321-020-00469-w) and [string2string](https://chemrxiv.org/articles/preprint/STOUT_SMILES_to_IUPAC_Names_Using_Neural_Machine_Translation/13469202/1) translation tasks, see the codes of [DECIMER](https://github.com/Kohulan/DECIMER-Image-to-SMILES) and [STOUT](https://github.com/Kohulan/Smiles-TO-iUpac-Translator). 
        * An improvement to the old genetic algorithm, the authors have also released [JANUS](https://arxiv.org/abs/2106.04011), which allows for more efficient optimization in the chemical space. JANUS makes use of [STONED-SELFIES](https://pubs.rsc.org/en/content/articlepdf/2021/sc/d1sc00231g) and a neural network for efficient sampling. 
        
        ## Tests
        `selfies` uses `pytest` with `tox` as its testing framework.
        All tests can be found in  the `tests/` directory. To run the test suite for
        SELFIES, install ``tox`` and run:  
        
        ```bash
        tox -- --trials=10000 --dataset_samples=10000
        ```
        
        By default, `selfies` is tested against a random subset
        (of size ``dataset_samples=10000``) on various datasets:
        
         * 130K molecules from [QM9](https://www.nature.com/articles/sdata201422)
         * 250K molecules from [ZINC](https://en.wikipedia.org/wiki/ZINC_database)
         * 50K molecules from a dataset of [non-fullerene acceptors for organic solar cells](https://www.sciencedirect.com/science/article/pii/S2542435117301307)
         * 160K+ molecules from various [MoleculeNet](http://moleculenet.ai/datasets-1) datasets
         * 36M+ molecules from the [eMolecules Database](https://www.emolecules.com/info/products-data-downloads.html).
           Due to its large size, this dataset is not included on the repository. To run tests 
           on it, please download the dataset into the ``tests/test_sets`` directory 
           and run the ``tests/run_on_large_dataset.py`` script. 
        
        ## Version History
        See [CHANGELOG](https://github.com/aspuru-guzik-group/selfies/blob/master/CHANGELOG.md).
        
        ## Credits
        
        We thank Jacques Boitreaud, Andrew Brereton, Nessa Carson (supersciencegrl), Matthew Carbone (x94carbone),  Vladimir Chupakhin (chupvl), Nathan Frey (ncfrey), Theophile Gaudin,
        HelloJocelynLu, Hyunmin Kim (hmkim), Minjie Li, Vincent Mallet, Alexander Minidis (DocMinus), Kohulan Rajan (Kohulan),
        Kevin Ryan (LeanAndMean), Benjamin Sanchez-Lengeling, Andrew White, Zhenpeng Yao and Adamo Young for their suggestions and bug reports,
        and Robert Pollice for chemistry advices.
        
        ## License
        
        [Apache License 2.0](https://choosealicense.com/licenses/apache-2.0/)
        
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