Enable quality and pace in your drug discovery workflow
Accurate experimental design, together with flawless execution and integration of consistent data, are critical factors for ensuring the fidelity of high-quality decision-making in drug discovery. Issues with experimental reproducibility are not new, and confidence in the accuracy of research findings remains low. Coupled with the current levels of operational vulnerability caused by the COVID-19 pandemic, these factors have prompted a fundamental reassessment of strategic priorities.
In this webinar, Daniel Thomas, Head of Discovery Biology at Arctoris, will examine how these challenges can be overcome through the utilization of specialist outsourced capabilities and will highlight how these can deliver the critical data sets to keep projects on time while ensuring the accuracy and precision required.
Daniel will also show how innovative, cutting-edge liquid handling, automation, and detection technologies can enable scientists to directly design, and remotely run, experiments to deliver this paradigm shift at an unprecedented scale and speed.
Key learning objectives
- Learn how collaborative externalisation can help ensure resource continuity without sacrificing data quality
- Discover the importance of eliminating variability to drive high-quality experimental outcomes
- Understand how empirical data-driven design and contextual information enable accurate integration and interpretation
- Gain insight into the practical, automated workflows that support these goals
Who should attend
- Those responsible for leading research groups where places of work have been temporarily shut (i.e. where experimental work is currently reduced or on hold)
- Companies assessing their externalisation strategies and looking at what is available in the 'marketplace'
- Lab managers and researchers interested in reducing cycle times without sacrificing data quality
- Researchers from virtual biotechnology or AI-driven groups who need real-world data to test their hypotheses
- Those looking to increase their understanding of the key role automation can play in robust data generation, even at low volumes