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 preprint arXiv:2507.09466
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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.
CCDD defines a joint multimodal diffusion on continuous representation space and discrete token space to combine expressivity with explicit token supervision for diffusion language models.
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.
Protein language models exhibit consistent depth inefficiency where most task-relevant computation occurs in a subset of layers, mirroring patterns in large language models.
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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.