Recurrent Neural Network using randomized SMILES strings to generate molecules
Code for the paper "A Deep Generative Model for Fragment-Based Molecule Generation" (AISTATS 2020)
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Code for training machine learning model for reaction condition prediction
A small, highly performant JavaScript component for parsing and drawing SMILES strings. Released under the MIT license.
Mol2vec - an unsupervised machine learning approach to learn vector representations of molecular substructures
A PyTorch-based knowledge distillation toolkit for natural language processing
:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps
Data for our paper "Chemical Reaction Practicality Judgment via Deep Symbol Artificial Intelligence"
RXNMapper: Unsupervised attention-guided atom-mapping. Code complementing our Science Advances publication on "Extraction of organic chemistry grammar from unsupervised learning of chemical reactions" (https://advances.sciencemag.org/content/7/15/eabe4166).
Reaction fingerprints, atlases and classification. Code complementing our Nature Machine Intelligence publication on "Mapping the space of chemical reactions using attention-based neural networks" (http://rdcu.be/cenmd).
