Some time ago we found the following paper https://arxiv.org/abs/1610.02415 so we decided to take a look at it and train the described model using ChEMBL. Lucky us, we also found two open source implementations of the model; the original authors one https://github.com/HIPS/molecule-autoencoder and https://github.com/maxhodak/keras-molecules . We decided to rely on the last one as the original author states that it might be easier to have greater success using it. What is the paper about? It describes how molecules can be generated and specifically designed using autoencoders. First of all we are going to give some simple and not very technical introduction for those that are not familiar with autoencoders and then go through a ipython notebook showing few examples of how to use it. Autoencoder introduction Autoencoders are one of the many different and popular unsupervised deep learning algorithms used nowadays for many different fields and purposes. These work wi...
The Organization of Drug Discovery Data
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