This is an application of personalization-weight-PageRank in drug discovery. It is designed to simplify the calculation of molecular dynamics simulations between large numbers of proteins and small molecule drug/pharmacophores based on Markov decision process (MDP). We use PageRank by python library NetworkX for multi-target drug discovery.
The whole process of this research is in experiment.py. It is complex and have no graph.
simple_example.py contains a simple example of the experiment. It contains graphes and is easy for learning.
python = 3.8.10
NetworkX
Pandas
NumPy
SciPy
OpenPyXL
Matplotlib
jupyter
notebook
You could install the environment by conda:
conda env create -f environment.yaml
Then it will create a conda environment pagerank.
Or install it in other conda environment:
conda install --yes --file requirements.txt
We recommend use jupyter notebook extension of VSCode for calculation. It is very intuitive.
The data has two parts:
- The docking data is listed in
/data. This means the affinity between drugs and targets. - The differentiation of targets by bioinformatics analysis. It is listed in the
.pyfile.
- Get all docking ranks of target proteins.
- For different time group, calculate the rank of single drug respectively.
- For different time group, calculate the rank of drug combinations.
The license of this research is MPL-2.0.
Liu F, Jiang X, Yang J, Tao J, Zhang M. A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia. Brief Bioinform (2022).
M. Baek, et al., Accurate prediction of protein structures and interactions using a three-track neural network, Science (2021).
I.R. Humphreys, J. Pei, M. Baek, A. Krishnakumar, et al, Computed structures of core eukaryotic protein complexes, Science (2021).