At our upcoming virtual Discovery Science meeting, we’ll hear from speakers on the theme: High-performance data meets high-performance computing. We’re increasingly seeing from the literature and our user community that combining quality data with computing power is changing drug discovery approaches. Here I want to share some examples of this theme.
Here we highlight a paper using CSD-Discovery to identify drug models for the treatment of complications due to diabetes and to define molecular features that can guide future drug design. This is part of our series highlighting examples of the Cambridge Crystallographic Data Centre (CCDC) tools in action by scientists around the world.
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.
With every challenge, there is opportunity. Looking into the vast challenge that is COVID-19 it can be difficult to see this, but the pandemic has forced us to contemplate new possibilities in all areas of life, including scientific research.
The world turned to the pharmaceutical industry for help, and the variety of responses makes us question; how did some get ahead while others struggled?