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Cheminformania

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    • Esben Jannik Bjerrum
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  • 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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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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