AI models can rapidly suggest the right genome edit or protein design. But an algorithm trained on digital datasets alone predicts how a sequence scores, rather than how it behaves. Candidates at the top of a model's list can misfold, aggregate, or express poorly once they reach a living cell.
This resource maps the design-build-test-learn cycle for AI-driven biological workflows. Synthesizing and testing AI-designed sequences closes the physical feedback loop, and one round's results become training data for the next.
Download this resource guide to explore
- Why models that perform well computationally can still fail in the lab
- How DNA synthesis fidelity shapes the quality of training data
- How one antibody program achieved 10x to 100x affinity gains in four iterative rounds
- Which services carry in silico designs through to model-ready data
