Neural Networks are interesting algorithms, but sometimes also a bit spooky. In this blog post I explore the possibilities for teaching the neural networks to generate completely
RDKit UGM 2016
I’m looking forward for the first to attend the RDKit user group meeting from 26-28 October 2016 in Basel, Switzerland. RDKit is an open source chemoinformatics toolkit
Learn how to map a simple Ames mutagenicity model to molecular features using RDkit.
Toxic compounds are most often something that we try to avoid when designing novel pharmaceutical compounds, so it could be nice to get a prediction if a
Learn how to hack RDKit to handle peptides with pseudo atoms
The chemoinformatics package Rdkit has is strength with handling small organic molecules. These molecules are characterized by a large diversity in chemical structures. A description of the
Never do this mistake when using Feature Selection
Feature selection is a powerful way of reducing the complexity of a machine learning or statistical model. But feature selection must be done in the right way,
Safer fitting through regularization
Last time a simple multiple linear regression (MLR) model was seriously overfitted to molecular solubility data. This time the concept of regularization will be tested. Recall that
Wash that gold: Modelling solubility with Molecular fingerprints….
Last blog entry the conversion between molecule and fingerprint was briefly touched upon. Now the fingerprints will be used as the basis for a simple attempt to
Fetch the Sherif! I found a fingerprint!
The headline is a bit misleading as it’s not fingerprints of criminals, but chemical fingerprints. Chemical fingerprinting is a way of converting drawn molecules into streams of
The Good, the Bad and the Ugly RDKit molecules
Rdkit is a nice cheminformatics toolkit with python bindings. Wildcard Pharmaceutical Consulting have over the years used it a lot for a couple of different projects in