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Calculation module of QEPPI

Project description

QEPPI

Quantitative Estimate Index for Compounds Targeting Protein-Protein Interactions

License GitHub Clones Hits tests PyPI version

Calculation QEPPI with using Google Colab

We have made it so that you can use Google Colab to calculate QEPPI from SMILES without creating your own environment.
If you have a lot of SMILES to calculate, please convert the SMILES to SDF files.

Open In Colab

Mininal environment setup

We setup it on a Mac (macOS10.15.7), but I'm sure it will run fine on other platforms such as Linux.

# dependencies
pip install rdkit-pypi
pip install numpy
pip install pandas

We also confirmed that QEPPI works with Colab. (see notebook)

Clone QEPPI

Clone QEPPI repository when you are done with the setup.

git clone https://github.com/ohuelab/QEPPI.git

Test

Test it after git clone the QEPPI repository.
If the test passes, the QEPPI calculation has been successfully performed.
(We used pytest version is 6.2.2)

cd QEPPI
pytest -v

QEPPI calculation example

# for .sdf
python calc_QEPPI.py --sdf PATH_TO_YOUR_COMPOUND.sdf --out PATH_TO_OUTPUT.csv
# for .csv ("A column name of "SMILES" is required.")
python calc_QEPPI.py --csv PATH_TO_YOUR_COMPOUND.csv --out PATH_TO_OUTPUT.csv

Instalation using pip install

You can install it with pip install QEPPI.
First, you need to install with the dependencies (see Mininal environment setup).
The following sample code is available as an implementation example.

# QEPPI
pip install QEPPI
import QEPPI as ppi
from rdkit import Chem
from rdkit.Chem import SDMolSupplier

q = ppi.QEPPI_Calculator()
q.read()

# SMILES
smiles = "COC1=CC(=CC=C1NC(=O)[C@@H]1N[C@@H](CC(C)(C)C)[C@@](C#N)([C@H]1C1=CC=CC(Cl)=C1F)C1=CC=C(Cl)C=C1F)C(O)=O"
mol = Chem.MolFromSmiles(smiles)
print(q.qeppi(mol))
# 0.7862842663145835

# SDF
ppi_s = SDMolSupplier("PATH_TO_SDF/YOUR_COMPOUND.sdf")
ppi_mols = [mol for mol in ppi_s if mol is not None]
result = list(map(q.qeppi, ppi_mols))

Reference

  • Kosugi T, Ohue M. Quantitative estimate of protein-protein interaction targeting drug-likeness. In Proceedings of The 18th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2021). (accepted) ChemRxiv, Preprint. 2021. doi:10.33774/chemrxiv-2021-psqq4-v2
  • Kosugi T, Ohue M. Development of a quantitative estimate index for early-stage screening of compounds targeting protein-protein interactions. (under revision)

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