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We’ve recently demonstrated our Fragnet Search application in a SaferWorldbyDesign webinar.

In it we demonstrate using the web application and programmatically accessing the REST API behind it.

Take a look at the recording on YouTube.

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We’ve been working with the fragment network as part of our work with the fragment screening program at the Diamond Light Source for a couple of years now. The fragment network was conceived by Astex and provides a chemically intuitive way to identify similar compounds without the limitations of traditional fingerprint based similarity searching.

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One of our more interesting GitHub projects is the Docking Validation project. We use this to establish and document best practices in virtual screening tools (such as docking) and approaches to semi-automating and scaling these procedures.

We just completed a new ‘experiment’ that is related to our work at the Diamond Light Source’s XChem project which has done some amazing work on fragment based screening using XRay crystallography.

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You’ve probably created a machine image at some point. A base image for AWS that builds upon someone’s work by adding a particular version of Java or Python or a new utility. Did you create the image on AWS using an EC2 instance, login, run some yum or apt-get and then save it? Great, and if someone wants the source code for that image or you want to build a similar image on a different region or provider? Well, Packer is an IaC tool for automating the construction of machine images.

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The Python Jenkins module is a convenient wrapper for the Jenkins REST API that gives you control of a Jenkins server in a pythonic way. Here we’ll see how to grab all the jobs from a Jenkins server and also how these jobs can be re-created from the captured material.

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In this post we look at using buildah to generate container images that only contain what we want, no extra fluff. We show how this can let us generate truly small images that will load faster and be more secure, and do this without the need for the Docker daemon to be running.

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Here we’re going to be looking at the the idea of applying automation tools to the wider product development process. Tools that help you do this are part of a collection known as “Infrastructure as Code”, which refers to the the provisioning of compute instances (physical machines and their operating systems) and software applications using revision-controlled machine-readable text files.

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This series of posts describes how we can generate smaller Docker images. In the first post we outlined a common problem with container images - that they frequently contain artefacts that were needed to build the software or to install it into the container. We’ll show one approach that can be used to avoid this extra bloat, and so generate smaller and more secure containers.

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I’ve been in meetings, often driven by the root-cause-analysis of a software fault found in the field, where the topic of code coverage has cropped up. I’m sure many of us have been in similar meetings. On occasions I’ve also been asked to justify some of my apparently poor line coverage figures, where the percentage has fallen short of what was perceived by the inquisitor as acceptable.

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This is the first in a series of blog posts about building better Docker images.

Docker Inc is widely acknowledged for transitioning containers from geekdom to the real world inhabited by us developers, and did this by providing easy to use tools for building, sharing and running containers. Key to this is docker build command and the Dockerfile.

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Welcome to the Informatics Matters blog.

This is the first post of what will become a regular stream of information about our activities at Informatics Matters in providing solutions for scientific computing, including bioinformatics, genomics, cheminformatics and computational chemistry.

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