Peter Wood CCDC

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. 

The way that we use version numbers in our software applications is changing - and this should now be much simpler! This blog explains what's changed, and how you can make sure you have the most up to date version.

Each year we hold User Group Meetings, or UGMs, as a chance to meet with scientists from all areas of industry and academia who are using our software. This quick summary outlines what happens on the day, and why you should attend if you haven't before.

The CCDC Chemical Crystallography Prize for Younger Scientists (or 'CCDC Prize' for short) was created by the CCDC and the Chemical Crystallography Group (CCG) of the British Crystallographic Association (BCA) to both inspire and recognise excellence in crystallographic research coming from early career scientists in the UK. This prize is awarded every year on the basis of original research in the field of chemical crystallography or the application of crystallographic information to structural chemistry, including advances in instrumental, experimental, theoretical or computational techniques within this field.

Using data driven insights to inform the future of the CSD

With the Cambridge Structural Database (CSD) reaching a million structures earlier this year, there is now, more than ever, the opportunity to harness the power of this data through effective visualisation, analysis and extraction.

The introduction of the CSD Python API in 2015 has been transformative in structural science research both within the CCDC and across our user community. 

We are pleased to announce the latest update of the CSD software including enhanced protein handling features throughout the CSD portfolio and greatly improved searching capabilities in the CSD Python API.

We are excited to announce the addition of a polyhedral display style to our Mercury software!

Mercury offers a comprehensive range of tools for 3D structure visualisation, the exploration of crystal packing and the statistical analysis of CSD search data, providing an extensive array of options to aid the investigation and analysis of crystal structures. 

This release marks the beginning of a new chapter in the history of the Cambridge Structural Database (CSD). We have just completed a multi-year software development project to replace the search engine at the heart of our well-known search program ConQuest. This not only brings into alignment the search functionality behind ConQuest, Mercury, WebCSD and the CSD Python API, but it enables much more flexible, dynamic and advanced searching of the CSD in the future.

As we described earlier in the year, alongside our Access Structures/WebCSD system we launched a WebCSD Ideas voting page where you can log in and identify which features you’d most like to see added or improved in the interface. We're continuing to prioritise improvements to WebCSD directly driven by your input and feedback in this voting page and in person when we meet you at events. We launched the first of these directly user-driven improvements (full screen 3D viewer) earlier in 2018 and two more are now available in WebCSD (formula searching and structure search templates).