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    "info": {
        "author": "Peter St. John",
        "author_email": "peter.stjohn@nrel.gov",
        "bugtrack_url": null,
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            "Programming Language :: Python :: 3",
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        "description": "[![Build Status](https://travis-ci.org/NREL/nfp.svg?branch=master)](https://travis-ci.org/NREL/nfp)\n[![PyPI version](https://badge.fury.io/py/nfp.svg)](https://badge.fury.io/py/nfp)\n\n# Neural fingerprint (nfp)\n\nKeras layers for end-to-end learning on molecular structure. Based on Keras, Tensorflow, and RDKit. Source code used in the study [Message-passing neural networks for high-throughput polymer screening](https://arxiv.org/abs/1807.10363)\n\n## Related Work\n\n1. [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://arxiv.org/abs/1509.09292)\n2. [Neural Message Passing for Quantum Chemistry](https://arxiv.org/pdf/1704.01212.pdf)\n3. [Relational inductive biases, deep learning, and graph networks](https://arxiv.org/abs/1806.01261)\n4. [Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials](https://arxiv.org/abs/1806.03146)\n\n## (Main) Requirements\n\n- [rdkit](http://www.rdkit.org/docs/Install.html)\n- keras (github master, until [#11548](https://github.com/keras-team/keras/pull/11548) is included in a release)\n- tensorflow\n\n## Getting started\n\nThis library extends Keras with additional layers for handling molecular structures (i.e., graph-based inputs). There a strong familiarity with Keras is recommended.\n\nAn overview of how to build a model is shown in `examples/solubility_test_graph_output.ipynb`. Models can optionally include 3D molecular geometry; a simple example of a network using 3D geometry is found in `examples/model_3d_coordinates.ipynb`.\n\nThe current state-of-the-art architecture on QM9 (published in [4]) is included in `examples/schnet_edgeupdate.py`. This script requires qm9 preprocessing to be run before the model is evaluated with `examples/preprocess_qm9.py`.",
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