Webinar
Accelerating the Screening of Seized Drugs With Chromatography-Free Workflows
On-Demand
Attend this webinar to discover how direct analysis in real time (DART), in combination with high-resolution mass spectrometry (HRMS), allows the generation of reliable analytical results much faster and easier than alternative techniques.
Webinar
Ask Me Anything: Lab of The Future
On-Demand
The lab of the future will integrate the latest technologies to enable a more efficient, compliant and sustainable workplace for the next generation of researchers. From innovations such as automation and robotics to connectivity and the cloud, the lab of the future should integrate physical and virtual technology.
Webinar
Discover How Raw Materials Identification Testing Should Be
On-Demand
Raw materials identification (RMID) is a crucial regulatory step required before drug manufacturing. Raman spectroscopy is well-suited to making the process simpler, yet some spectrometers are challenged by light-blocking containers or struggle to reach compliance standards.
Webinar
Advancing mAb Stability Studies With Mass Photometry
On-Demand
In this webinar, you’ll discover how mass photometry can be used for forced antibody degradation assessment, a crucial aspect of research and development for recombinant mAb therapeutics.
Online Event
Laboratory of the Future 2024
On-Demand
Advances in automation, digitization and data modeling are changing how labs are built and run from the ground up. Hear about the technologies helping to drive digital transformation and laboratory efficiencies.
Webinar
Innovations in Multiomics Research for Life Sciences
On-Demand
Unearthing novel drug targets with robust data support and a deep understanding of biological processes remains a pivotal challenge in drug discovery. In this engaging webinar, distinguished speakers Aaron Mitchell and Rasmus Wernersson from ZS Associates will unveil a comprehensive method that significantly enhances target prediction performance through the integration of multiomics data, coupled with a data-driven, cell-type specific network biology approach.
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