Advancing AI for biology: Teaching models to design and characterize antibodies
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Machine learning
Advancing AI for biology: Teaching models to design and characterize antibodies
Three new papers from Amazon Bio Discovery address bottlenecks in AI-driven antibody engineering, from benchmarking binding predictors to experimentally validating de novo design.
By Melih Yilmaz , Luca Giancardo , Yue Zhao , Edward Lee , Chuanyui Teh , Fangda Xu , Gordon Trang
September 21, 2026
11 min read
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Key takeaways
Amazon Bio Discovery has developed three AI approaches to accelerating antibody drug design: MochiBind for ranking binding candidates by sequence alone, CA-MAP for predicting antibody developability properties while accounting for lab-specific batch effects, and an agent-guided design system validated experimentally on a novel cancer target. MochiBind reframes binding affinity prediction as relative ranking rather than absolute values, achieving 10% higher accuracy than structure-based competitors while enabling 100-fold faster inference, scoring 200,000 antibody pairs in 13 seconds on a CPU. CA-MAP addresses batch effects through context-aware learning, maintaining 0.99 correlation accuracy even with simulated batch offsets where standard models degrade to 0.58, while using 1/200th as many parameters and running 200x faster than competing large language models. The end-to-end design campaign successfully generated 46 validated strong binders against a desmoplastic small round cell tumor target with no prior structural or antibody information.
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Monoclonal antibodies are one of the workhorses of biopharmaceutical development, with over 100 FDA-approved drugs and well-established manufacturing, regulatory, and clinical-development pathways. Yet conventional antibody discovery remains hampered by mounting costs and long timelines, typically six to twelve months to get from a target to a lead candidate. By designing and characterizing therapeutic antibodies computationally, AI promises to make development cheaper, faster, and more flexible. But scientific questions abound. Development of an antibody-based drug hinges on three factors: the best binding site on the target, which candidates bind to it most tightly, and whether any of them can survive manufacturing and the clinic. For each, the field has predictive models that do well on familiar targets and assays but considerably worse on unfamiliar ones. Benchmarks built around in-distribution accuracy have made that gap difficult to measure — and to close. Three papers from our science team at Amazon Bio Discovery, an AI-powered application that gives scientists access to biological AI models and integrated lab services to design and test novel drug candidates, tackle research questions about each of these three factors. Two are peer-reviewed journal papers on prediction: ranking candidates by binding strength and flexibly predicting developability . The third brings prediction into an end-to-end design process , navigates the selection of binding sites with an...
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Notability
notability 6.0/10Amazon research post on antibody design AI.