Modern screening techniques, such as CRISPR-Cas gene editing, phenotypic imaging, and single-cell omics, have significantly expanded the possibilities for identifying new disease pathways and therapeutic targets. This process is further aided by insights from mining vast amounts of public and private data.

Even with the aid of advanced computational methods and artificial intelligence, an increase in the number of targets equates to an increase in costs associated with proving or refuting each target. How, then, can pharmaceutical companies assess the performance of these targets while leveraging the resources required for this process?

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