A 2022 report estimates at 16 billion dollars the amount of money invested into drug candidates that failed between Phase 1 and submission, illustrating – if need be – that drug discovery remains a long, arduous, and expensive process. Reviewing, analyzing data and understanding reasons for those failures to make better decisions is now a global endeavor for companies and the focus is now on finding the “right” drug candidates at the optimal Intersection of target binding, bioavailability, and pharmacological activity. However, when the Data is disparate and widely distributed across an organization, this task can be daunting, time-consuming and very costly.
- How scientists can make better decisions, faster by unifying vast amounts of data from any source into a single view, in a self-service fashion
- Ways in which chemists and biologists alike can see a holistic view of data "at the Intersection" in minutes rather than months
- How to leverage software to cover most of the common scientific analysis workflows without a steep learning curve
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