AI is transforming biologics discovery by accelerating early-stage processes such as hit identification, screening, and lead optimization, helping reduce the 12–15 year, multi-billion-dollar path to market. Yet adoption faces obstacles: fragmented data, complex integration, infrastructure demands, and reproducibility challenges. ENPICOM’s blueprint addresses these through three pillars—robust data foundations, workflow automation, and standardized AI integration—enabling scalable, compliant, and efficient MLOps. With this approach, pharma can streamline discovery, improve candidate optimization, and unlock faster, more cost-effective therapeutic development.

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