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Code for Coherent blending of biophysics-based knowledge with Bayesian neural networks for robust protein property prediction

The scripts to reproduce the experiments in the paper are:

  1. funcprior/bin/gb1.py
  2. funcprior/bin/gfp.py
  3. funcprior/bin/solubility.py

Dependencies:

  • pytorch
  • tensorflow
  • deepchem
  • PyTDC
  • gpytorch
  • seaborn
  • numpy
  • pandas

Installing Dependencies: To create a conda environment with all the required dependencies run: conda env create -f environment.yml Note: You may need to modify the cuda versions for pytorch and tensorflow.

Once the dependencies are installed, install the provided funcprior package: cd funcprior pip install .

Datasets: The GB1 dataset provided is from https://data.caltech.edu/records/g58c2-zzb91 under an MIT license. The GFP dataset provided is from https://github.com/gitter-lab/nn4dms/tree/master/data under an MIT license The solubility dataset is provided by the Therapeutics Data Commons (https://tdcommons.ai) under an MIT license. The original data was AqSolDB and was provided under a CCO 1.0 license.