The promise of AI in drug discovery often breaks down when models leave the lab. Why? Because most AI is trained on In-Distribution (IID) data - clean, static, and predictable. But real-world biology is messy, full of patient variability and rare signals, known as Out-of-Distribution (OOD) data. Ignoring these OOD outliers - the patient who doesn't respond, the unexpected toxicology - means you are missing the most important signals for breakthroughs. This isn't just a technical problem; it's a strategic one that costs R&D time and budget. Scaling model size won't fix it; we need to fix the data.
We must shift focus from algorithms to the data that fuels them. Elucidata introduces AI Labs, a new operating system for Data-Centric AI. This platform ensures your models are robust, reliable, and capable of handling biological complexity. We provide the blueprint for mastering OOD data through three pillars: high-quality, harmonized data; diverse, federated learning; and biological constraints built into the AI. Join us to learn how to future-proof your pipeline, accelerate drug translation, and leverage the full, complex value of your biological data with Elucidata.
The promise of AI in drug discovery often breaks down when models leave the lab. Why? Because most AI is trained on In-Distribution (IID) data - clean, static, and predictable. But real-world biology is messy, full of patient variability and rare signals, known as Out-of-Distribution (OOD) data. Ignoring these OOD outliers - the patient who doesn't respond, the unexpected toxicology - means you are missing the most important signals for breakthroughs. This isn't just a technical problem; it's a strategic one that costs R&D time and budget. Scaling model size won't fix it; we need to fix the data.
We must shift focus from algorithms to the data that fuels them. Elucidata introduces AI Labs, a new operating system for Data-Centric AI. This platform ensures your models are robust, reliable, and capable of handling biological complexity. We provide the blueprint for mastering OOD data through three pillars: high-quality, harmonized data; diverse, federated learning; and biological constraints built into the AI. Join us to learn how to future-proof your pipeline, accelerate drug translation, and leverage the full, complex value of your biological data with Elucidata.
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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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This session gives biomedical researchers and data scientists the practical steps to build stronger, more reliable AI for drug discovery.
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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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