We’ve known since 2016 that LSTM networks can be used to generate novel and valid SMILES strings of novel molecules after being trained on a dataset of
Transformer for Reaction Informatics – utilizing PyTorch Lightning
In the last blogpost I covered how LSTM-to-LSTM networks could be used to “translate” reactants into products of chemical reactions. Performance was however not very good of
Deep Learning Reaction Prediction with PyTorch
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 Keras as a stand-alone is now no longer active developed, but are instead now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
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
SMILES enumeration and vectorization for Keras
The SMILES enumeration code at GitHub has been revamped and revised into an object for easier use. It can work in conjunction with a SMILES iterator object