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Showing posts from July, 2017

Using autoencoders for molecule generation

Some time ago we found the following paper 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 and . 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

ChEMBL web services webinar 4pm 12th July

We are pleased to announce the next webinar in our ChEMBL webinar series: ChEMBL, programmatically (part of the EMBL-EBI, programmatically: take a REST from manual searches webinar series) will be held at 4pm (BST) on 12th July . The webinar will provide an overview of the ChEMBL API and its use, including how to execute API calls from the browser; where to find documentation; how to user filtering and pagination; available output formats; and scripting examples in Python, Bash and R.  We will also give examples of how the API can be used to create reusable web components and integrated into tools such as KNIME and Slack. The webinar will assume a degree of familiarity with the data in ChEMBL, so new users are advised that an introductory ChEMBL webinar is also available: To register for this webinar, please see The ChEMBL Team