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The CCDC offers a selection of products and services free of charge for the benefit of the scientific community, which includes a free version of Mercury. While the free version supports several functionalities, many popular features are only available with a licence. Here we explain the differences between the free and paid licence versions of Mercury.
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I’m a Research and Applications Scientist on the Materials Science team at CCDC, and I recently taught a session at the Rigaku School for Practical Crystallography, which ran from June 7–18, 2021. The school focused on practical applications of software, techniques and technologies for crystallography. This blog contains links to my recorded modules as well as a self-assessment quiz you can use to check what you’ve learned.
Welcome to CSD University!
We are thrilled to introduce you to our new collection of on-demand educational resources: CSD University (CSDU) – or actually, to the first module of this collection. In this blog you will learn more about the CSDU idea, format (which includes a completion certificate), and the first module, Visualisation 101.
The existence of various molecular arrangements that occur in the solid-state is called polymorphism. Identifying polymorphs is important for risk management purposes and exploring the polymorphic landscape to identify the most stable forms is an important step during early-stage drug development. As part of our Tools in Action blog series highlighting the use of CCDC tools by scientists around the world, we recently showed how a research team used the Cambridge Structural Database (CSD) and the Hydrogen Bond Propensity (HBP) tool to characterize two polymorphs of an anti-inflammatory drug and predict the existence of additional forms. Here we present more information about how the HBP tool works to see if you can use it to assess polymorphs.
The way we communicate science can have a significant impact on the way will be received, and ultimately how it will contribute to advancing knowledge. Videos can often better illustrate the message we want to send across when dealing with molecular structures - and it's simple to make them.
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?
We are excited to announce that we will be launching H-bond Coordination Quick-View in Mercury as part of our upcoming 2020.0 release in December! This latest development will enable quick and easy hydrogen-bond likelihood analysis using coordination numbers for the observed structure.
