Source code of our paper:
Cardoso-Silva, J., Papageorgiou, L. G. & Tsoka, S. Network-based piecewise linear regression for QSAR modelling. J. Comput. Aided. Mol. Des. 33, 831–844 (2019). http://link.springer.com/10.1007/s10822-019-00228-6
(update 14/03/2021: We're re-organizing the notebooks, a new more didatic version of this source code should be available soon)
Checkout this notebook for a newer practical example.
-
Download the source code either from the
Code
button above to download zip or checkout project from Github. -
Create a folder
data
and moveMaster Chemical List - annotated.xlsx
to the data folder. -
Install Docker. Read the instructions for Mac, Ubuntu, Windows
-
Install
docker-compose
-
Open a terminal and type
docker-compose build
to let Docker download all the required dependencies and libraries automatically -
Type
docker-compose up
to start Jupyter notebook. Click on the link that will show up on the terminal and run the notebooks:OSM-S4 - Notebook 01 - Preprocessing.ipynb
OSM-S4 - Notebook 02 - Running modSAR.ipynb
By default, modSAR will solve the equations with GLPK solver but it can also work with CPLEX if you have an academic or commercial license to run MIP algorithms. In that case, pass solver_name="cplex"
when creating an instance ModSAR
class.