Machine Learning


Understanding protein-ligand recognition is crucial in drugs discovery.

Most of the features used for describing such interactions are parametric.

This practically means that, if there are not suitable parameters for a particular atom, functional group or interaction, either the calculation fails or a rough approximation is provided.

We wanted to know whether or not non-parametric chemical descriptors could compete with parametric ones.


We tested two different non-parametric descriptors sets by using machine learning algorithms.

One of such sets provided better results than those obtained with state-of-the-art parametric sets.

A Python 3 / Bash automatic workflow was developed that:

  1. Downloads and fixes the PDB files.
  2. Minimises the protein-ligand binding site.
  3. Splits the testing, validation and training sets according to different criteria.
  4. Trains machine learning models.
  5. Determines which, among thousands of descriptors, contribute more to model accuracy.
  6. Plots the results.


The results have been published in a peer-reviewed scientific journal.