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.
Fast protein backbone generation with se (3) flow matching
6 Pith papers cite this work. Polarity classification is still indexing.
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OGPP is a particle flow-matching method using orbit-space canonicalization and geometric paths that achieves lower error and fewer steps than prior approaches on 3D benchmarks.
PAR is a multi-scale autoregressive transformer framework for protein backbone generation that uses coarse-to-fine prediction, noisy context learning, and flow-based decoding to achieve high-quality unconditional and zero-shot conditional outputs.
D-Flow applies multi-modality flow matching and a mirror-image data augmentation to generate D-peptides with 10.2% higher sequence identity and 24.31% top affinity on the PepMerge benchmark.
OMNI-PoseX presents a unified vision model using open-vocabulary perception and SO(3)-aware reflected flow matching to deliver state-of-the-art 6D pose estimation with real-time performance for embodied tasks.
Flow Matching is a generative modeling framework with mathematical foundations, design choices, extensions, and open-source PyTorch code for applications like image and text generation.
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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Generative Modeling with Orbit-Space Particle Flow Matching
OGPP is a particle flow-matching method using orbit-space canonicalization and geometric paths that achieves lower error and fewer steps than prior approaches on 3D benchmarks.
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Protein Autoregressive Modeling via Multiscale Structure Generation
PAR is a multi-scale autoregressive transformer framework for protein backbone generation that uses coarse-to-fine prediction, noisy context learning, and flow-based decoding to achieve high-quality unconditional and zero-shot conditional outputs.
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D-Flow: Multi-modality Flow Matching for D-peptide Design
D-Flow applies multi-modality flow matching and a mirror-image data augmentation to generate D-peptides with 10.2% higher sequence identity and 24.31% top affinity on the PepMerge benchmark.
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OMNI-PoseX: A Fast Vision Model for 6D Object Pose Estimation in Embodied Tasks
OMNI-PoseX presents a unified vision model using open-vocabulary perception and SO(3)-aware reflected flow matching to deliver state-of-the-art 6D pose estimation with real-time performance for embodied tasks.
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Flow Matching Guide and Code
Flow Matching is a generative modeling framework with mathematical foundations, design choices, extensions, and open-source PyTorch code for applications like image and text generation.