yq is a portable command-line YAML, JSON, XML, CSV and properties processor

Code for KDD'20 "Generative Pre-Training of Graph Neural Networks"

Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

SchNetPack - Deep Neural Networks for Atomistic Systems

Graph neural networks for molecular design.

A library for graph deep learning research

Python version of the amazing Reaction Mechanism Generator (RMG).

DECIMER 1.0: Deep Learning for Chemical Image Recognition using Transformers

Quantum Nature

Graph Network for protein-protein interface

Graph4nlp is the library for the easy use of Graph Neural Networks for NLP

Message Passing Neural Networks for Molecule Property Prediction

JARVIS-Tools: an open-source software package for data-driven atomistic materials design

Just another Cron alternative with a Web UI, but with much more capabilities. It aims to solve greater problems.

FFmpeg for browser and node, powered by WebAssembly

Modern CSS framework based on Flexbox

Single-file alternative to python-requests

CGRs, molecules and reactions manipulation

Mol2vec - an unsupervised machine learning approach to learn vector representations of molecular substructures

Python module for quantum chemistry

A Python library which allows construction and manipulation of complex molecules, as well as automatic molecular design and the creation of molecular databases.

chemicals: Chemical database of Chemical Engineering Design Library (ChEDL)

Codebase to accompany the paper A Look Inside the Black Box: Using Graph-Theoretical Descriptors to Interpret a Continuous-Filter Convolutional Neural Network (CF-CNN) trained on the Global and Loc…

Fluid dynamics component of Chemical Engineering Design Library (ChEDL)

Thermodynamics and Phase Equilibrium component of Chemical Engineering Design Library (ChEDL)

The Biorefinery Simulation and Techno-Economic Analysis Modules; Chemical Process Simulation Under Uncertainty

BioSTEAM's Premier Thermodynamic Engine

ML4Chem: Machine Learning for Chemistry and Materials

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials,…

active learning for accelerated high-throughput virtual screening