A-CODE presents a fully atomic one-stage multimodal diffusion model for protein co-design that claims superior unconditional generation performance over prior one- and two-stage models plus a tenfold success-rate gain on hard binder-design tasks.
La- Proteina: Atomistic protein generation via partially latent flow matching.Arxiv e-print, arXiv:2507.09466 [cs.LG]
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Steerable NODEs extend manifold neural ODEs by coupling base flow on homogeneous spaces with parallel transport of features in associated bundles, achieving G-equivariance under invariant conditions.
DISCO co-designs protein sequence and structure to produce functional heme enzymes that catalyze several new-to-nature carbene-transfer reactions at activities exceeding prior engineered enzymes.
Yeti is a compact tokenizer for protein structures that delivers strong codebook use, token diversity, and reconstruction while enabling from-scratch multimodal generation of plausible sequences and structures with 10x fewer parameters than ESM3.
citing papers explorer
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A-CODE: Fully Atomic Protein Co-Design with Unified Multimodal Diffusion
A-CODE presents a fully atomic one-stage multimodal diffusion model for protein co-design that claims superior unconditional generation performance over prior one- and two-stage models plus a tenfold success-rate gain on hard binder-design tasks.
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Steerable Neural ODEs on Homogeneous Spaces
Steerable NODEs extend manifold neural ODEs by coupling base flow on homogeneous spaces with parallel transport of features in associated bundles, achieving G-equivariance under invariant conditions.
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General Multimodal Protein Design Enables DNA-Encoding of Chemistry
DISCO co-designs protein sequence and structure to produce functional heme enzymes that catalyze several new-to-nature carbene-transfer reactions at activities exceeding prior engineered enzymes.
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Yeti: A compact protein structure tokenizer for reconstruction and multi-modal generation
Yeti is a compact tokenizer for protein structures that delivers strong codebook use, token diversity, and reconstruction while enabling from-scratch multimodal generation of plausible sequences and structures with 10x fewer parameters than ESM3.