nfp

Neural fingerprint (nfp)

Keras 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

Related Work

• Convolutional Networks on Graphs for Learning Molecular Fingerprints
• Neural Message Passing for Quantum Chemistry
• Relational inductive biases, deep learning, and graph networks
• Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials

(Main) Requirements

• rdkit
• tensorflow

Getting started

This library extends Keras with additional layers for handling molecular structures (i.e., graph-based inputs). There a strong familiarity with Keras is recommended.

An 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.

The 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.