How do predictive models empower drug discovery?

A conversation with Adam Sanford, Ph.D., and Orr Ravitz, Ph.D., from CAS

Close-up of transparent spherical capsules illuminated in blue, each containing intricate, embedded structures resembling cells or circuits.

In today’s rapidly evolving drug discovery landscape, predictive models have emerged as essential tools to accelerate workflows by simulating and predicting biological activity, drug-target interactions, and much more. The utility of these models is highly dependent on the quality and management of the data upon which they are built. At the forefront of this technological revolution is CAS, whose CAS BioFinder Discovery Platform™ is powered by advanced predictive models. To understand how the accuracy of these models leads to true insights for drug discovery scientists, we spoke with Adam Sanford, Ph.D., Director of the Life Sciences Division, and Orr Ravitz, Ph.D., Senior CAS BioFinder® Product Manager, to delve into the rigorous data management strategies that make CAS a leader in the field.

CAS: What is the CAS approach to data integration, normalization, and harmonization to support your predictive models?

CAS: How does that approach help your models benefit drug discovery researchers?

CAS: What are some of the biggest challenges you’ve faced in developing the models?

CAS: As publications and data are constantly emerging, how does CAS ensure these models remain current?

CAS: Is there anything on the horizon for CAS BioFinder and your predictive models that you’re particularly excited about?

CAS: What makes this approach to predictive modeling in drug discovery unique? 

CAS: If you had a magic wand to change anything about the drug discovery process, what would you change?