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AI Antibody Design vs Human Design: A Conceptual Overview

1Why Antibody Design Matters2How Humans Design Antibodies3How AI Learns to Design Antibodies4Comparing AI and Human Design5Evidence, Limits, and Open Questions
Comparing AI and Human Design

Complementary, Not Competing — and Still Experimental

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The comparison table is the heart of this page: read the two columns as two different failure modes. The AI pipeline cannot decide what is worth building and it produces predictions rather than measurements. The human pipeline cannot test candidates at computational scale. Because the failures are different, they do not cancel out — using AI to search broadly and human judgment to select and interpret is not a compromise, it is each side doing what it is actually good at. And notice what stays the same in both columns: the experiment. A predicted score is a hypothesis about a physical interaction, and so is a carefully reasoned human design. Only expressing the antibody and measuring it turns either one into knowledge.
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AI and human design are complementary because they fail in different places: AI is limited by judgment and by the need for experimental confirmation, while human design is limited by how many candidates can be physically built and tested.

Where each approach breaks down

AI pipeline

  • Cannot decide which target or which trade-off matters
  • Produces predictions, not measurements
  • Least reliable on cases unlike its training data

Human pipeline

  • Limited by physical rounds of building and assaying
  • Cannot explore sequence space at computational scale
  • Depends on individual expert experience and intuition

A high predicted binding score is a hypothesis, not a result. So is a well-reasoned human design. In both cases, expressing the antibody and measuring its binding is what turns a candidate into knowledge.

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