I found some interesting toxicology datasets from the Tox21 challenge, and wanted to see if it was possible to build a toxicology predictor using a deep neural

I found some interesting toxicology datasets from the Tox21 challenge, and wanted to see if it was possible to build a toxicology predictor using a deep neural
As covered before, chemical space is huge. So it could be nice if this multidimensional molecular space could be reduced and visualized to get an idea about
Neural networks are interesting models underlying much of the newest AI applications and algorithms. Recent advances in training algorithms and GPU enabled code together with publicly available
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
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
When I have been working with chemical databases and import of molecules I have encountered numerous problems with the way chemical structures are drawn. Most often the
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
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
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
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