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The cost of sequencing a human genome has plummeted, but the challenge remains interpretation. We will explore how large AI models are moving from assisting diagnostics to actively designing novel drugs and personalized treatments, and what this means for enterprise R&D budgets and ethical guidelines.

Setting the stage:

Why is AI essential for genomics now?

What is the current bottleneck in drug discovery?

How are LLMs being fine-tuned for chemical and biological structures?

Specific AI applications that are cutting discovery time.

How is AI used in de novo protein design?

What is a "digital twin" of a patient, and how does it speed up clinical trials?

How do we secure massive genomic datasets (PII)?

What happens when models discover biases in existing genetic research?

Is personalized medicine an investment or an insurance policy?

Which AI capabilities (e.g., small molecule prediction vs. large molecule discovery) yield the highest ROI?

What is the single most important hire an R&D department can make in the next 12 months to prepare for the AI-native bio-future?

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Source: https://www.linkedin.com/pulse/ai-native-bio-enterprise-mapping-next-decade-personalized-medicine-fv3xc