Skip to main content

A qm descriptor prediction package

Project description


GitHub license Documentation Status PyPI version

A trained multitask constraint message passing neural networks for QM atomic/bond property predictions as described in the paper Regio-Selectivity Prediction with a Machine-Learned Reaction Representation and On-the-Fly Quantum Mechanical Descriptors.

QM descriptors under B3LYP/def2svp level of theory that can be predicted with this model:

  1. Hirshfeld partial charge
  2. Neucleuphilic Fukui indices
  3. Electrophilic Fukui indices
  4. NMR shielding constants
  5. Bond lengths
  6. Bond orders

Documentation: Documentation of qmdesc is available at


  • RDKit


For all installations, we recommend using conda to get the necessary rdkit dependency:

conda install -c rdkit rdkit
pip install qmdesc

Or from envrioment.yml

conda create --name qmdesc --file environment.yml

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for qmdesc, version 1.0.6
Filename, size File type Python version Upload date Hashes
Filename, size qmdesc-1.0.6.tar.gz (11.4 kB) File type Source Python version None Upload date Hashes View
Filename, size qmdesc-1.0.6-py3-none-any.whl (19.8 MB) File type Wheel Python version py3 Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page