HBNDmodel

Hydrogen bond network dimensionality (HBND) describes how hydrogen-bond intermolecular interactions extend in a three-dimensional structure.

The network arrangement is thought to influence properties where hydrogen bonding plays critical role, such as crystal stability, mechanical behaviour and compound tabletability performance.

The hydrogen bond network dimensionality problem can be formulated as a four-class classification task, and the four possible network dimensionality outcomes are schematically represented below.

We provide a ready to usemodel for Hydrogen Bond Network Dimensionality prediction.

Input: list of SMILES of the molecules to be predicted

Output: a list of (class, SMILES) pairs

Performance: 59% accuracy (compared to a 25% random threshold)

See the reference for more details.

Disclamer:

1. The model is only prepared to predict the HBND of single chemical component organic crystals.

2. Using the model as is may only be suitable for initial initial screening of large libraries. The trust of output predictions can be increased by subjecting the model to a confidence threshold. This approach may enable further single compound HBND profilling (link).

See the reference for more details.

Dependencies

The code should be run using Python 3.

 RDkit
 Pandas
 NumPy
 JobLib

Dependency installation via conda:

  $ conda install pandas numpy pickle joblib matplotlib scikit-learn

  $ conda install -c conda-forge rdkit

Installation

Via pip:

  $ pip install -i https://test.pypi.org/simple/ HBNDmodel

References

Paper under revision