Long time ago in a GPU far-far away, the deep learning rebels are happy. They have created new ways of working with chemistry using deep learning technology
Never do these mistakes when comparing regression models
Some time ago I stumbled upon some work by Patrick Walters which shows that correlation coefficients have a rather large standard error when the sample sets sizes
Learn how to improve SMILES based molecular autoencoders with heteroencoders
Earlier I wrote a blog post about how to build SMILES based autoencoders in Keras. It has since been a much visited page, so the topic seems
Deep Chemometrics: Deep Learning for Spectroscopy
During my postdoc project at the Chemometrics and Analytical Technology section at Copenhagen University I worked with modeling of spectroscopical data with PLS models. Chemometrics is “the
Master your molecule generator: Seq2seq RNN models with SMILES in Keras
UPDATE: Be sure to check out the follow-up to this post if you want to improve the model: Learn how to improve SMILES based molecular autoencoders with
Learn how to teach your computer to "See" Chemistry: Free Chemception models with RDKit and Keras
The film Inception with Leonardo Di Caprio is about dreams in dreams, and gave rise to the meme “We need to go deeper”. The title has also
Better Deep Learning Neural Networks with SMILES Enumeration of Molecular Data
The process of expanding an otherwise limited dataset in order to more efficiently train a neural network is known as Data Augmentation For images there have been
Teaching Computers Molecular Creativity
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
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