Skip to content

Latest commit

 

History

History
53 lines (41 loc) · 1.6 KB

README.md

File metadata and controls

53 lines (41 loc) · 1.6 KB
Raw

MolSolv: An AI-based model for aqueous solvation energy prediction.

image

Requirements

  • Python 3.6
  • openbabel >= 3.0
  • numpy 1.18.1
  • scipy
  • pandas 0.25.3
  • freesasa
  • pytorch
  • pytorch geometric

You also can create the python environment by conda configure file:

conda env create -f environment.yaml

If you run torch-sparse with error, please uninstall the package torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric:

pip uninstall torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric

and then reinstall them:

pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.10.0+cpu.html

Usage

python predict_smd_solv.py -h

usage: predict_smd_solv.py [-h] [--smi SMI] [--fmax FMAX] [--cores CORES]
                           [--num_confs NUM_CONFS] [--output OUTPUT]

calculate solvation energy for small molecules

optional arguments:
  -h, --help            show this help message and exit
  --smi SMI             the molecular smiles
  --fmax FMAX           The convergence criterion is that the force on all
                        individual atoms should be less than fmax
  --cores CORES         the number of cpu for calculatuon
  --num_confs NUM_CONFS
                        the number of conformation for solvation energy
                        prediction
  --output OUTPUT       the output file name

Or you can access the web server of MolSolv--URL