Public articles linked to the same research event.
Amazon Science This article presents three efforts from Amazon Bio Discovery: MochiBind, a sequence-only predictor that reframes binding affinity as pairwise comparison aggregated by TrueSkill into a global ranking, achieving higher pairwise accuracy than every structure-based baseline on four held-out antigens and scoring 200,000 antibody pairs in roughly 13 seconds on a CPU; CA-MAP, a context-aware multi-property predictor that uses example antibodies in the prompt to absorb batch offsets, holding a 0.99 correlation under a simulated batch effect where standard fine-tuning falls to 0.
This article presents three efforts from Amazon Bio Discovery: MochiBind, a sequence-only predictor that reframes binding affinity as pairwise comparison aggregated by TrueSkill into a global ranking, achieving higher pairwise accuracy than every structure-based baseline on four held-out antigens and scoring 200,000 antibody pairs in roughly 13 seconds on a CPU; CA-MAP, a context-aware multi-property predictor that uses example antibodies in the prompt to absorb batch offsets, holding a 0.99 correlation under a simulated batch effect where standard fine-tuning falls to 0.
This article presents three efforts from Amazon Bio Discovery: MochiBind, a sequence-only predictor that reframes binding affinity as pairwise comparison aggregated by TrueSkill into a global ranking, achieving higher pairwise accuracy than every structure-based baseline on four held-out antigens and scoring 200,000 antibody pairs in roughly 13 seconds on a CPU; CA-MAP, a context-aware multi-property predictor that uses example antibodies in the prompt to absorb batch offsets, holding a 0.99 correlation under a simulated batch effect where standard fine-tuning falls to 0.
This article presents three efforts from Amazon Bio Discovery: MochiBind, a sequence-only predictor that reframes binding affinity as pairwise comparison aggregated by TrueSkill into a global ranking, achieving higher pairwise accuracy than every structure-based baseline on four held-out antigens and scoring 200,000 antibody pairs in roughly 13 seconds on a CPU; CA-MAP, a context-aware multi-property predictor that uses example antibodies in the prompt to absorb batch offsets, holding a 0.99 correlation under a simulated batch effect where standard fine-tuning falls to 0.