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.
De novo design of high-affinity protein binders with alphaproteo
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ProtDBench standardizes protein binder design evaluation using wet-lab data, exposing verifier biases, metric dependencies, and trade-offs between success rate, speed, and structural diversity.
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.
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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ProtDBench: A Unified Benchmark of Protein Binder Design and Evaluation
ProtDBench standardizes protein binder design evaluation using wet-lab data, exposing verifier biases, metric dependencies, and trade-offs between success rate, speed, and structural diversity.
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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.
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Towards an AI co-scientist
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.