antibody-design

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#antibody-design

@arcinstitute: Congrats to @BrianHie, @SynBioGaoLab, and team on Germinal, now out in @NatureBiotech. Their pipeline designs epitope-t…

X AI KOLs Following · 2026-06-24 Cached

Arc Institute announces Germinal, a generative AI system for de novo antibody design published in Nature Biotechnology. It designs epitope-targeted antibodies with nanomolar affinity testing only tens of designs per target, making custom antibody design more accessible.

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#antibody-design

@nablabio: Today, we expand zero-shot drug design beyond binding to the design of multifunctional medicines, the intracellular pro…

X AI KOLs Following · 2026-06-23 Cached

Nabla Bio unveils JAM-2, a model for zero-shot drug design achieving atomic-precision, computationally designed multispecific antibodies and dual-variant KRAS multispecifics with high potency and selectivity, validated with Cryo-EM and wet-lab experiments.

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#antibody-design

AgForce Enables Antigen-conditioned Generative Antibody Design

arXiv cs.LG · 2026-05-22 Cached

This paper identifies three failure modes in existing antibody design methods (antigen blindness, vocabulary collapse, convergence to marginal distribution) and proposes AgForce, a novel encoder-decoder architecture using graph neural networks and mixture density networks, achieving state-of-the-art binding quality and sequence recovery on the Chimera-Bench benchmark.

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#antibody-design

ConTact: Contact-First Antibody CDR Design via Explicit Interface Reasoning

arXiv cs.LG · 2026-05-22 Cached

ConTact introduces a contact-then-act architecture for antibody CDR design that explicitly decomposes the task into interface reasoning, contact prediction, and contact-gated sequence generation, achieving state-of-the-art structural quality and epitope awareness on the Chimera-Bench benchmark.

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#antibody-design

Conditional generation of antibody sequences with classifier-guided germline-absorbing discrete diffusion

arXiv cs.LG · 2026-05-11 Cached

This paper introduces a discrete diffusion model with a novel 'germline absorbing' modification to improve conditional antibody sequence generation. It addresses germline bias in protein language models and demonstrates superior performance in optimizing antibody binding affinity and developability compared to existing methods like EvoProtGrad.

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