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    • Esben Jannik Bjerrum
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    • Esben Jannik Bjerrum

pdChemChain – linking up chemistry processing, easily!

Esbenbjerrum/ October 1, 2024

I’ve been working on a project intermittently for some time, and I recently packaged it up and published it on GitHub, hoping it could be useful to

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Generating Unusual Molecules with Genetic Algorithms Part 2: Leveraging MolLL for Enhanced Generation

Esbenbjerrum/ April 15, 2024

In a previous blogpost: (Generating Unusual Molecules with Genetic Algorithms), I showcased the propensity of a genetic algorithm to generate unusual molecules. This illustrates that this generative

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Update to Scikit-Mol: the power of community and open-source

Esbenbjerrum/ April 14, 2024

We’ve just updated Scikit-Mol[1] to version 0.3.0. Scikit-Mol was covered in a previous blogpost. The big news in this update is the support for pandas output (and

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Generating Unusual Molecules with Genetic Algorithms

Esbenbjerrum/ February 4, 2024

I’ve long been working with generative models, mostly centered around SMILES-based deep learning models. However, I’ve been wanting to try out genetic algorithms for some time. Using

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Scikit-Mol – Easy Embedding of RDKit into Scikit-Learn

Esbenbjerrum/ December 20, 2023

I’d like to share a post about a project I’ve been involved in developing—Scikit-Mol. I believe it’s a noteworthy project deserving attention. It has already been featured

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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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