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
A deeper look into chemical space with neural autoencoders
In the last blogpost the battle tested principal components analysis (PCA) was used as a dimensionality reduction tool. This time we’ll take a deeper look into chemical
Molecular neural network models with RDKit and Keras in Python
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
Machine Learning optimization of Smina cross docking accuracy
In the two previous blog posts Ligand docking with Smina and Never use re-docking for …, it was demonstrated how easy it is to dock a small ligand
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
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