In this blogpost I’ll show how to predict chemical reactions with a sequence to sequence network based on LSTM cells. It’s the same principle as IBM’s RXN
Using GraphINVENT to generate novel DRD2 actives
I have been writing a lot about how to use SMILES together with deep learning architectures such as RNNs and LSTM networks to perform various cheminformatic and
Building a simple SMILES based QSAR model with LSTM cells in PyTorch
Last blog-post I showed how to use PyTorch to build a feed forward neural network model for molecular property prediction (QSAR: Quantitative structure-activity relationship). RDKit was used
Building a simple QSAR model using a feed forward neural network in PyTorch
In my previous blogposts I’ve entirely been using Keras for my neural networks. Keras as a stand-alone is now no longer active developed, but are instead now
Master your molecule generator 2. Direct steering of conditional recurrent neural networks (cRNNs)
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
Learn how to make a jupyter notebook widget for annotation of atom properties
Not so long ago Greg Landrum published a blog post with an example of how the SVG rendering from RDKit in a jupyter notebook can be
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
