I&I diseases involve complex interactions between immune cells, inflammatory pathways, and the tissues where disease develops. Even with increasingly precise, mechanism-driven therapies, patients with the same diagnosis can respond very differently to treatment.
The field has made major progress in understanding disease and developing targeted therapies. Yet questions around why some approaches work, why others fall short, and what drives differences in patient response remain open.
As researchers look more closely at immune-cell states, disease heterogeneity, tissue biology, and underlying mechanisms, new opportunities are emerging to rethink how I&I therapies are discovered and developed.
Join us for a discussion with experts leading drug discovery and translational programs across I&I indications as we take stock of current approaches, explore where the field is headed, and discuss what could shape the next generation of I&I drug development.
I&I diseases involve complex interactions between immune cells, inflammatory pathways, and the tissues where disease develops. Even with increasingly precise, mechanism-driven therapies, patients with the same diagnosis can respond very differently to treatment.
The field has made major progress in understanding disease and developing targeted therapies. Yet questions around why some approaches work, why others fall short, and what drives differences in patient response remain open.
As researchers look more closely at immune-cell states, disease heterogeneity, tissue biology, and underlying mechanisms, new opportunities are emerging to rethink how I&I therapies are discovered and developed.
Join us for a discussion with experts leading drug discovery and translational programs across I&I indications as we take stock of current approaches, explore where the field is headed, and discuss what could shape the next generation of I&I drug development.
Scaling clinico-genomic data integration: Large pharmaceutical organizations working with external data providers used Polly to build interoperable clinico-genomic data products 6x faster.
Although purchased datasets are often labeled as "clean," they still lack interoperability—Polly's pipelines bridge this gap with robust integration and harmonization.
Information Retrieval: Drug safety monitoring teams used Polly's Knowledge Graph powered co-scientist to conversationally retrieve the right cohorts & assess drug response—cutting discovery time by 70%.
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If you’re working with complex biological data, you may be asking:
Can generative AI truly assist in scientific reasoning, not just data analysis?
What does it mean for hypothesis generation, literature review, or even designing experiments?
Could this accelerate—not replace—my discovery pipeline?
Whether you're skeptical, curious, or already experimenting with AI in your lab—this is a session designed to ground your understanding in evidence, not speculation.
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