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In the 2021.3 release, we launched four new subsets: Electron Diffraction, Polymorphs, Hydrates, and High Pressure. We're also updating the navigation and API access to all CSD Subsets. In this blog, we look at real-world use cases for the new CSD Subsets and tips for making the most of the new functionalities.
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A solid form landscape shows you the possible polymorphs and stoichiometries your solid form system could take. This allows you to identify risks and find opportunities to impact stability and properties. So how can you foresee solid forms before you have a crystal structure? Here we see how the CSD Landscape Generator in Mercury makes it possible.
In our 2020.1 CSD Release, we launched the Aromatics Analyser – our first feature in Mercury based on a Neural Network that leverages deep learning. The tool allows you to quantitatively assess aromatic ring interactions and their likely contribution to the stability of a crystal structure. In this blog, let’s explore why it’s so important to understand aromatic interactions using ibuprofen and benzoic acids as examples.
Molecules that have different molecular packing arrangements despite identical chemical composition are said to be polymorphic. The variety of possible forms, each an allomorph (or polymorph), presents opportunities and challenges in various fields – including the drug industry and agriculture and forestry. In this blog, we talk to United States Department of Agriculture (USDA) Research Scientist Emeritus1 and the Editor-in-Chief of Cellulose, Dr Alfred D. French about his work with cellulose, which has multiple polymorphs (allomorphs).
I’m a Research and Applications Scientist on the Materials Science team at CCDC. In this short blog and accompanying video, I walk through how to make the most of your CSD-Enterprise licence using the tools in CSD-Materials. In the first half of the video, I focus on the Cambridge Structural Database (CSD), highlighting its integrations with other databases and how to best use it for geometric analysis. In the second half of the video, I discuss applications of the CSD-Materials software, demonstrating how the different tools within the suite can help with pharmaceutical risk analysis.
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.
Here we highlight a paper by authors at Universidad de los Andes, Universidad Industrial de Santander and Universidad Santo Tomás using 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. This is part of our series highlighting examples of the Cambridge Crystallographic Data Centre (CCDC) tools in action by scientists around the world.
Here we summarise a publication from our collaboration with Pfizer, which demonstrates the use of the healthcheck methodology to reduce risk and select the most stable form across three different APIs.
Here we spotlight a recent publication which shows how CSD data supported a team from Novartis and NanoMEGAS to analyse a metastable form of the antihistamine drug Loratadine. Part of our series highlighting examples of our data and software in use around the world.
The field of Crystal Structure Prediction (CSP) has grown dramatically over the last few decades, but today I want to highlight a recent paper which takes a new approach. The recent publication from Montis et al makes use of new informatics tools to assess surface roughness (rugosity) and proposes a relationship between this and ease of crystallisation.
