The drug discovery research industry is seeing a growing prevalence of chemically-modified, biologically-based therapeutic candidates. 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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