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
Learn how to improve SMILES based molecular autoencoders with heteroencoders
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
Master your molecule generator: Seq2seq RNN models with SMILES in Keras
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
SMILES enumeration and vectorization for Keras
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
Learn how to teach your computer to "See" Chemistry: Free Chemception models with RDKit and Keras
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
Better Deep Learning Neural Networks with SMILES Enumeration of Molecular Data
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
Tune your deep tox neural network with free tools
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
A deep Tox21 neural network with RDKit and Keras
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
A deeper look into chemical space with neural autoencoders
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
Molecular neural network models with RDKit and Keras in Python
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