Skip to content

Cheminformania

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

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

Read More

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

Read More

Never do these mistakes when comparing regression models

Esbenbjerrum/ August 25, 2019

Some time ago I stumbled upon some work by Patrick Walters which shows that correlation coefficients have a rather large standard error when the sample sets sizes

Read More

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

Read More

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

Read More

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

Read More

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

Read More

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

Read More

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

Read More

Machine Learning optimization of Smina cross docking accuracy

Esben Jannik Bjerrum/ May 19, 2016

In the two previous blog posts Ligand docking with Smina and Never use re-docking for …, it was demonstrated how easy it is to dock a small ligand

Read More

Search

Search for:

Recent Comments

  1. esbenbjerrum on A deep Tox21 neural network with RDKit and KerasJanuary 22, 2025

    Yes, it's a single-task network. For a multi-task network, you would need to increase the number of output-neurons to fit…

  2. Elon on A deep Tox21 neural network with RDKit and KerasJanuary 20, 2025

    If I understand correctly, it seems you have used a single-label approach 'SR-MMP' instead of a multi layer approach using…

  3. esbenbjerrum on Generating Unusual Molecules with Genetic AlgorithmsNovember 24, 2024

    Yes, of course that is possible;-) I wrote a follow-up blogpost using molecular log-likelihood estimation to accomplish just that Generating…

Popular Pages

  • Peeking into the chemical space using free tools
  • Non-conditional De Novo molecular Generation with Transformer Encoders
  • Machine Learning optimization of Smina cross docking accuracy
  • Cheminformatics in Excel part 2: RDKit4Excel
  • Scripting Molecular Mechanics Calculations using Tinker and sdf2xyz2sdf

Tags

AI AI drug discovery Autodock Vina autoencoders benchmarking Chemception chemical deep learning Cheminformatics CNNs computational chemistry Computational Creativity Cross Docking Deep Learning de novo design docking Drug Discovery GUI Heteroencoders Inverse QSAR ipywidgets Jupyter Keras Ligand Docking Long short term memory Machine Learning molecule generation Molecules Neural Networks Optimization PCA Python Pytorch QSAR RDKit rDock Reaction Prediction recurrent neural networks sklearn SMILES generation Spectroscopy Theano Transformers Tuning vina xlwings

The software is provided “as is”, without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and non-infringement. In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software.

Mastodon

© Esben Jannik Bjerrum, 2015-2019. Unauthorized use and/or duplication of this material without express and written permission from this site’s author and/or owner is strictly prohibited. Excerpts and links may be used, provided that full and clear credit is given to Esben Jannik Bjerrum and Cheminformania/Wildcardconsulting.dk with appropriate and specific direction to the original content.

Search

Search for:
https://www.youtube.com/watch?v=bvgz1CUzTYs
2015 - 2022 © Powered by Theme Vision.