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Cheminformania

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    • About Cheminformania
    • Esben Jannik Bjerrum
  • Blog
  • About
    • About Cheminformania
    • Esben Jannik Bjerrum

Non-conditional De Novo molecular Generation with Transformer Encoders

Esbenbjerrum/ May 13, 2021

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

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Transformer for Reaction Informatics – utilizing PyTorch Lightning

Esbenbjerrum/ April 24, 2021

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

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Deep Learning Reaction Prediction with PyTorch

Esbenbjerrum/ March 29, 2021

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

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Using GraphINVENT to generate novel DRD2 actives

Esbenbjerrum/ November 2, 2020

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

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Building a simple SMILES based QSAR model with LSTM cells in PyTorch

Esbenbjerrum/ June 6, 2020

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

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Building a simple QSAR model using a feed forward neural network in PyTorch

Esbenbjerrum/ May 1, 2020

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 Keras as a stand-alone is now no longer active developed, but are instead now

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Master your molecule generator 2. Direct steering of conditional recurrent neural networks (cRNNs)

Esbenbjerrum/ November 12, 2019

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

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Learn how to improve SMILES based molecular autoencoders with heteroencoders

Esben Jannik Bjerrum/ October 4, 2018

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

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Deep Chemometrics: Deep Learning for Spectroscopy

Esben Jannik Bjerrum/ May 26, 2018

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

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Master your molecule generator: Seq2seq RNN models with SMILES in Keras

Esben Jannik Bjerrum/ December 14, 2017

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

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