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

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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Learn how to teach your computer to "See" Chemistry: Free Chemception models with RDKit and Keras

Esben Jannik Bjerrum/ November 28, 2017

The film Inception with Leonardo Di Caprio is about dreams in dreams, and gave rise to the meme “We need to go deeper”. The title has also

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Better Deep Learning Neural Networks with SMILES Enumeration of Molecular Data

Esben Jannik Bjerrum/ March 23, 2017

The process of expanding an otherwise limited dataset in order to more efficiently train a neural network is known as Data Augmentation For images there have been

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Tune your deep tox neural network with free tools

Esben Jannik Bjerrum/ February 7, 2017

In another blog post I demonstrated how to build a deep neural network with Keras in Python to model some toxicity dataset from the Tox21 challenge. The

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A deep Tox21 neural network with RDKit and Keras

Esben Jannik Bjerrum/ January 15, 2017

I found some interesting toxicology datasets from the Tox21 challenge, and wanted to see if it was possible to build a toxicology predictor using a deep neural

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A deeper look into chemical space with neural autoencoders

Esben Jannik Bjerrum/ January 3, 2017

In the last blogpost the battle tested principal components analysis (PCA) was used as a dimensionality reduction tool. This time we’ll take a deeper look into chemical

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Molecular neural network models with RDKit and Keras in Python

Esben Jannik Bjerrum/ December 6, 2016

Neural networks are interesting models underlying much of the newest AI applications and algorithms. Recent advances in training algorithms and GPU enabled code together with publicly available

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Teaching Computers Molecular Creativity

Esben Jannik Bjerrum/ November 7, 2016

Neural Networks are interesting algorithms, but sometimes also a bit spooky. In this blog post I explore the possibilities for teaching the neural networks to generate completely

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