This is a recording of the March 2017 Global Health Compound Design meeting. A webinar demonstrating using Jupyter, the free iPython notebook.
How to get started
Accessing Open Source Malaria data
Calculating physicochemical properties and plotting
Predicting AMES activity.
I've now written a couple of Jupyter notebooks and one of the issues that has come up is how to share the notebooks in a way that ensures the results will be reproducible in an environment when updates to components occur regularly.
Binder is a collection of tools for building and executing version-controlled computational environments that contain code, data, and interactive front ends, like Jupyter notebooks. It's 100% open source.
At a high level, Binder is designed to make the following workflow as easy as possible
- Users specify a GitHub repository
- Repository contents are used to build Docker images
- Deploy containers on-demand in the browser on a cluster running Kubernetes
Common use cases include:
- sharing scientific work
- sharing journalism
- running tutorials and demos with minimal setup
- teaching courses
If you want to find out more have a look at this blog post by the developers.
I've been experimenting with the use of Jupyter Notebooks (aka iPython Notebooks) as an electronic lab notebook but also a means to share computational models. The aim would be to see how easy it would be to share a model together with the associated training data together with an explanation of how the model was built and how it can be used for novel molecules.
The Ames test is a widely employed method that uses bacteria to test whether a given chemical can cause mutations in the DNA of the test organism. More formally, it is a biological assay to assess the mutagenic potential of chemical compounds. PNAS. 70 (8): 2281–5. doi
In this first notebook a random forest model to predict AMES activity is described….
The Molecular Design Toolkit is an open source environment that aims to seamlessly integrated molecular simulation, visualization and cloud computing. It offers access to a large and still-growing set of computational modelling methods with a science-focused Python API, that can be easily installed using PIP. It is ideal for building into a Jupyter notebook. The API is designed to handle both small molecules and large bimolecular structures, molecular mechanics and QM calculations.
There are a series of Youtube videos describing some of the functionality in more details, starting with this introduction.
This blog post looks very interesting, a notebook environment for coding, data visualisation based on Juypter (aka iPython) notebooks
With nteract, you can create documents, that contain executable code, textual content, and images, and convey a computational narrative. Unlike Jupyter, your documents are stand-alone, cross-platform desktop applications, providing a seamless desktop experience and offline usage.
More details are on GitHub.