Skip to content

Latest commit

 

History

History
64 lines (50 loc) · 2.56 KB

README.md

File metadata and controls

64 lines (50 loc) · 2.56 KB
Raw

dGPredictor

==================================

Requirements:

  1. Python 3.8.10
  2. RDkit (http://www.rdkit.org/)
  3. pandas (https://pandas.pydata.org/)
  4. matplotlib (https://matplotlib.org/stable/users/installing.html)
  5. Scikit-learn (https://scikit-learn.org/stable/)
  6. Streamlit (https://streamlit.io/)
  7. Openbabel (https://anaconda.org/openbabel/openbabel)
  8. ChemAxon's Marvin >= 5.11
  9. Pulp

Installation

  1. Python 3.8.10 (https://www.python.org/downloads/windows/) Recommended-
  • Create anaconda environment using command "conda create -n dGPredictor python=3.8 ipython"
  • activate the env using command "conda activate dGPredictor" or "source activate dGPredictor"
  1. RDkit
  • type command "conda install -c conda-forge rdkit" in your dGPredictor env to install rdkit
  1. Pandas
  • "conda install pandas"
  1. matplotlib
  • "conda install -c conda-forge matplotlib"
  1. Scikit-learn
  • use command "pip install -U scikit-learn"
  1. Streamlit
  • use command "pip install -U streamlit"
  1. Openbabel
  • run "conda install -c conda-forge openbabel"
  1. ChemAxon's Marvin (PkA value estimation)
  • Marvin is only required for adding structures of novel metabolites/compounds that are not in the KEGG database
  • instructions (https://chemaxon.com/products/marvin/download)
  • add "cxcalc.bat (macOS) /cxcalc.exe (Windows)" to PATH and also in "./CC/chemaxon.py" file
  • you will need to get a license to use ChemAxon (it is free for academic use)
  1. Pulp
  • use command "pip install -U pulp"

==================================

Running web-interface locally using streamlit

  • Model generation: Run "model_gen.py" using "python model_gen.py" once to create dGPredictor model file :- (Running this might take some time)
  • run "streamlit run ./streamlit/main.py" from dGPredictor folder
  • running KEGG reaction (doesn't require ChemAxon's Marvin) : copy paste the reaction equation into reaction section and click search

Gibbs free energy prediction use automated group decomposition method

  • Step 1: decompose the metabolites based on smiles files (see function decompse_ac in decompose_groups.py or notebook )
  • Step 2: create group changes vectors (i.e. reaction rules) based on group changes in metabolites of reactions (see get_rxn_rule in decompose_groups.py)
  • Step 3: linear regression, Ridge Regression and Bayesian Ridge Regression in "predict.py"
  • Step 4: Multiple regression models in notebook "analysis_dGPredictor.ipynb"

Pathway design using novoStoic

  • Run "mini_novoStoic.py" to see an example to design pathways for Isobutanol synthesis

demo

dGPredictor Demo