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DataFAIR 2023: Data Quality- The Cornerstone of Effective Generative AI in Drug Discovery

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.

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What we will discuss

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Real-World Applications We’ll Cover

How a diagnostics company reduced biomarker analysis prep time from 3 weeks to 4 hours.

How clinical ops teams use our AI-powered co-scientist chatbot to query curated data conversationally—cutting discovery time by 70%.

How Python SDKs and scalable APIs are helping biostatisticians and engineers feed AI models, dashboards, and regulatory reports—all from the same source of truth.

Meet the Experts of this discussion
Key Takeaways
How data providers ensure adherence to quality standards through validation and compliance.
How GUI-based workflows, CLI tools, and collaborative workspaces enable streamlined data ingestion and synchronization at scale.
Understand how automated pipelines assess conformance, plausibility, and consistency, ensuring high-quality, AI-ready data products.
Key Takeaways
Reduce operational costs by streamlining data delivery through reusable, governed products.
Accelerate diagnostic development and clinical trial execution by delivering compliant, high-quality data at scale.
Improve audit readiness and regulatory confidence through governed data products and built-in quality assurance.
Equip cross-functional teams to act on trusted data—faster, and with greater confidence.
Who Should Attend?

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