On the use of linear models to predict partition coefficients
Simple and extensive linear models to predict water/octanol partition coefficients for small molecules.
Models
There are two models in two subfolders:
-
model- the extensive modelling efforts in the MSG500 course (autumn 2017) -
phys- simple model presented at my docent lecture and at the Combine Engineering Journal
Experimental data
The experimental training data was collected from the SI from the following article:
Automated Parametrization of the Coarse-Grained Martini Force Field for Small Organic Molecules. The Table S1 was manually copied to the file training.xlsx
The experimental test data was collected from the Minnesota database and copied to the file testing.xlsx. This is a subset used in the paper "Solvation free energies and partition coefficients with the coarse-grained and hybrid all-atom/coarse-grained MARTINI models".
Collecting molecular properties
The properties of the molecules were collected using the Indigo package
python props/collect_props.py training.xlsx
Overlap of experimental data sets
The indices of the molecules in the training data that is also in the test data was created by
python props/compare_smi.py training.xlsx testing.xlsx
and the indices are available in overlap.dat
The experimental training data without this overlap is available in training_filtered.xlsx.