Intel / article

Advancing AI for biology: Teaching models to design and characterize antibodies

Source: amazon.science
Date: Published Sep 21, 2026

Context

Amazon reports an antibody-design workflow that produced 46 strong binders against a target associated with a rare pediatric cancer. In its research account, an uses biological-analysis tools to recommend binding regions, generative models propose designs, and a selection process prioritizes 100,000 candidates for laboratory screening.

Researchers displayed the candidates on yeast cells and screened for attachment to the target. Of 116 candidates retained after sorting, none bound an unrelated control protein; individual measurements identified the 46 strong binders. This provides laboratory evidence of target binding. Whether those candidates can become safe, effective medicines remains a separate development question.

Key takeaways · AI-distilled

MochiBind, Amazon's antibody-binding predictor, reframes the task from predicting absolute binding affinity to ranking which of two candidates binds more tightly — a pairwise approach the team found more robust across assays than absolute-value predictors.
Tested on four held-out antigens (TIGIT, PD-1, HER2, SARS-CoV-1 RBD) with strict cross-antigen validation, MochiBind beat every structure baseline on pairwise accuracy by ~10% average, and scored 200,000 antibody pairs in ~13s on a CPU — 100x faster than rivals.
CA-MAP, a second predictor, corrects for lab-to-lab batch effects via in- examples from the same lab rather than retraining: under a simulated batch effect, standard 's correlation with ground truth dropped from 0.99 to 0.58, while CA-MAP held at 0.99.
CA-MAP is dramatically cheaper than the baseline it beats: about 182,000 trainable parameters versus TxGemma's 40 million, and roughly 200 times faster per prompt at .
In a campaign against a novel pediatric-cancer target with no prior antibody data, Amazon's hotspot-agent and three generative models produced 288,000 candidates; after yeast-display screening and two sorting rounds, 46 of 116 survivors were confirmed strong, specific binders.

Terms in this piece · Glossary

AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.
inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.

Read the source www.amazon.science