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
- 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
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.