Generative AI models propose far more protein variants than a lab can synthesize and test individually. A pooled DNA library collapses that bottleneck, taking thousands of designed sequences into a single wet-lab screening run.
This compendium traces the role of pooled DNA libraries in protein engineering. An infographic outlines the design-build-test-learn cycle, a white paper reports accuracy and uniformity for 650 bp and 1.8 kb pools, and a case study shows how wet-lab antibody screening data feeds back into AI model predictions.
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- Where synthetic DNA enters an AI protein design workflow
- How much of a 3,800-sequence pool survived to sequencing
- What a hundred-thousand-sequence antibody library takes to run
