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        "author": "Mario Krenn",
        "author_email": "mario.krenn@utoronto.ca, alan@aspuru.com",
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            "License :: OSI Approved :: Apache Software License",
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        "description": "## SELFIES\n\nSELFIES (SELF-referencIng Embedded Strings) is a general-purpose, sequence-based,\nrobust representation of semantically constrained graphs. It is based on a Chomsky\ntype-2 grammar, augmented with two self-referencing functions. A main objective is\nto use SELFIES as direct input into machine learning models, in particular in\ngenerative models, for the generation of graphs with high semantical and syntactical\nvalidity.\n\nSee the paper at arXiv: https://arxiv.org/abs/1905.13741\n\nThe code presented here is a concrete application of SELFIES in chemistry, for\nthe robust representation of molecule. We show the encoding and decoding of three\nmolecules from various databases, and the generation of a new, random molecule\nwith high semantical and syntactical validity.\n\n### Installation\nYou can install SELFIES via\n```\npip install selfies\n```\n\n### Examples\nSeveral examples can be seen in examples/selfies_example.py. Here is a simple encoding and decoding:\n\n```python\nfrom selfies import encoder, decoder\n\ntest_molecule1='CN1C(=O)C2=C(c3cc4c(s3)-c3sc(-c5ncc(C#N)s5)cc3C43OCCO3)N(C)C(=O)C2=C1c1cc2c(s1)-c1sc(-c3ncc(C#N)s3)cc1C21OCCO1' # non-fullerene acceptors for organic solar cells\nselfies1=encoder(test_molecule1)\nsmiles1=decoder(selfies1)\n\nprint('test_molecule1: '+test_molecule1+'\\n')\nprint('selfies1: '+selfies1+'\\n')\nprint('smiles1: '+smiles1+'\\n')\nprint('equal: '+str(test_molecule1==smiles1)+'\\n\\n\\n')\n```\n\n- an example of SELFIES in a generative model can be seen in the directory 'VariationalAutoEncoder_with_SELFIES\\'. There, SMILES datasets are automatically translated into SELFIES, and used for training of a variational autoencoder (VAE).\n\n### Python version\nfully tested with Python 3.7.1 on\n- 134.000 molecules at QM9 database (https://www.nature.com/articles/sdata201422)\n- 250.000 molecues from the ZINC database (https://en.wikipedia.org/wiki/ZINC_database)\n- 72 million molecules from PubChem (https://pubchem.ncbi.nlm.nih.gov/)\n- 50.000 molecules for organic solar cells (https://www.sciencedirect.com/science/article/pii/S2542435117301307)\n- 1 million molecules from organic chemical reactions (https://pubs.rsc.org/en/content/articlehtml/2018/sc/c8sc02339e)\n\nsupported:\n- Python 3.7.2, 3.7.1, 3.6.8, 3.6.7, 2.7.15\n\n\n",
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