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The drug discovery research industry is seeing a growing prevalence of chemically-modified, biologically-based
therapeutic candidates1. This trend creates urgent and novel challenges for data management and analysis informatics
software, necessary for supporting cross-functional teams of scientists bridging across biology and chemistry. We
discuss key challenges and how to address them, such as rigour in scientific definition of candidate therapeutics and
opportunities to automate and streamline data analysis workflows. The goal is to ensure scientists have the highest
quality data to work with, and the time they need to drive drug discovery innovation.
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