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Gen AI has the potential to transform the drug discovery field. However, these models need to be trained with quality datasets before they can be productionized. Using an inadequately trained model in this context can result in inaccurate predictions, unviable outcomes, and significant project expenses. In this session, Dr. Jha discusses the importance of data quality in training Gen AI models and its role in enhancing the robustness and reliability of target prediction in the pharmaceutical industry.
Gen AI has the potential to transform the drug discovery field. However, these models need to be trained with quality datasets before they can be productionized. Using an inadequately trained model in this context can result in inaccurate predictions, unviable outcomes, and significant project expenses. In this session, Dr. Jha discusses the importance of data quality in training Gen AI models and its role in enhancing the robustness and reliability of target prediction in the pharmaceutical industry.
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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