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.
E., Patani, H., Danson, A
5 Pith papers cite this work. Polarity classification is still indexing.
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ProtDBench is a new evaluation benchmark that standardizes protein binder design assessment, reveals verifier-dependent bias in structure predictors, and compares generative methods under fixed 24-hour and diversity-aware criteria.
Proteo-R1 decouples an MLLM-based understanding expert that selects functional residues from a diffusion-based generation expert that builds protein structures under those explicit constraints.
A multi-agent AI system generates novel biomedical hypotheses that show promising experimental validation in drug repurposing for leukemia, new targets for liver fibrosis, and a bacterial gene transfer mechanism.
ADIOS applies opponent shaping in a meta-learning setup to create antibodies that target current and future viral variants while biasing evolution toward weaker strains, demonstrated in Absolut! simulations.
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Proteo-R1: Reasoning Foundation Models for De Novo Protein Design
Proteo-R1 decouples an MLLM-based understanding expert that selects functional residues from a diffusion-based generation expert that builds protein structures under those explicit constraints.