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.

How to run the notebooks

1. Download the source code either from the Code button above to download zip or checkout project from Github.

2. Create a folder data and move Master Chemical List - annotated.xlsx to the data folder.

3. Install Docker. Read the instructions for Mac, Ubuntu, Windows

4. Install docker-compose

5. Open a terminal and type docker-compose build to let Docker download all the required dependencies and libraries automatically

6. 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.