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    • Esben Jannik Bjerrum
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    • 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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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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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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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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SMILES enumeration and vectorization for Keras

Esben Jannik Bjerrum/ December 1, 2017

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

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