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New EditJumps framework open-sources antibody optimization models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed EditJumps, an open-source implementation of edit-based generative models for antibody lead optimization. This new framework replicates and improves upon existing methods like Edit Flows and EvoFlows by providing a single, generalist antibody editor trained on a large dataset. EditJumps can propose homolog-like variants of seed sequences in a zero-shot manner, addressing the need for code and complete training specifications in generative biology. AI

IMPACT This research provides an open-source tool for antibody lead optimization, potentially accelerating drug discovery and development.

RANK_REASON The item is a research paper detailing a new open-source implementation of generative models for antibody optimization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New EditJumps framework open-sources antibody optimization models

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The item is a research paper detailing a new open-source implementation of generative models for antibody optimization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Gabriel B\'en\'edict, Melanie Buechler, Gerard Riera-Sol\`a, Chlo\'e de Ancos, Yves Gaetan Nana Teukam, Moritz Freidank ·

    When Edit Flows are Edit Jumps: replicating Edit Flows and EvoFlows

    arXiv:2609.18745v1 Announce Type: cross Abstract: Antibody lead optimization calls for a small, bounded set of edits to an existing candidate: substitutions, but also insertions and deletions. Edit-based generative models are the only ones that allocate such an edit budget withou…

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