Advancing AI for biology: Teaching models to design and characterize antibodies
Amazon Science
Amazon Bio Discovery’s research team published three papers introducing AI methods to better predict binding and developability properties and to design antibodies or nanobodies for experimentally validated candidates. MochiBind scored 200,000 antibody pairs in roughly 13 seconds on a CPU, reporting more than a 100-fold inference speedup over competing methods. The work shifts antibody evaluation toward cross-antigen relative binding and batch-effect-aware context prediction, and it combines an epitope hotspot agent with de novo generative design to produce lab-validated hits against a novel cancer target.
Why it matters
Three new papers from Amazon Bio Discovery address bottlenecks in AI-driven antibody engineering, from benchmarking binding predictors to experimentally validating de novo design.
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