On 16th June news outlets around the world reported the fantastic results seen in trials using dexamethasone in critically ill COVID-19 patients. This quick briefing explores the chemistry and history of this compound, and what could be coming next.
The Aromatics Analyser is a new feature available in Mercury as part of the CSD-Materials and CSD-Enterprise suites. It was launched with our last release 2020.1 in April. Our users have been asking questions during our virtual events, as the What’s Up customer webinar, and we decided to share with you some of the most common questions.
Reflections on the IYPT in Crystals project - including the competition winners, educational resources you can use at home, and what's next for the fantastic entries collected from across the community.
We are pleased to announce the May 2020 data update of the Cambridge Structural Database (CSD) is now available! This data update brings you 10,188 new structures (10,697 new entries) and increases the total size of the CSD to over 1,048,000 structures (1,067,000 entries).
The Aromatics Analyser feature was released in our 2020.1 update, as part of the CSD-Materials suite. This short video shows you how to use this feature, and how to interpret the results.
In recent years, we have noticed an increase in the number of structures deposited to the Cambridge Structural Database (CSD) that are measured with electron diffraction techniques. As of the beginning of 2020, approximately 50 electron structures have been added to the CSD. Since this field of research is rapidly developing, we thought it timely to investigate all the electron studies in the database to ensure they can be easily located and have worked to identify any structures that were missed during the initial data curation process.
One of the major developments in the 2020.1 CSD Release is the addition of the CSD Pipeline Pilot component collection, which will allow you to build custom tools for analysing CSD structural data without writing code.
As well as allowing research to be done faster and more efficiently, this should remove barriers to entry and allow more people to create custom analyses.
Machine learning is a fast growing area of active research within structural science and it is particularly effective in the crystallographic structural sciences due to the wealth of highly accurate structural data available. A key part of machine learning though is having effective molecular descriptors to represent complex chemical information about molecules and structures into easily machine-interpretable vectors of numbers to feed into machine learning algorithms.
We live in exciting times for Artificial Intelligence (AI) - with the rise of new and easy to implement Machine Learning (ML) algorithms. Many of us would sooner trust a GPS to take us from point A to point B than consult a map ourselves, and robots are already being used to perform medical procedures. But what do all of these advanced techniques and algorithms mean for us as scientists and how can we use them to advance science? Presumably, many would ask if AI approaches can help, or even replace scientific experiments?
A few months ago, watching the news of COVID-19 spreading, we knew it would not be safe to hold our user group meeting at a hotel in Cambridge, MA as planned. Rather than cancelling, we moved this to become a virtual event which went ahead on the same date, 24th April 2020.