[upstream-sync] README.md from ai-boost/awesome-ai-for-science@60eb5f81 [catalogue]

2026-07-09 08:25:17 +00:00
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 title: "Readme"
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-upstream_sha: 408a2a18
-imported_at: 2026-07-08
+upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/60eb5f81/README.md
+upstream_sha: 60eb5f81
+imported_at: 2026-07-09
 prompt_class: catalogue
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 - [Proteina-Complexa](https://github.com/NVIDIA-Digital-Bio/Proteina-Complexa) - Flow-based generative model for atomistic protein binder design with test-time optimization, SOTA on binder benchmarks (ICLR 2026 Oral, NVIDIA)
 - [PXDesign (ByteDance, 2025)](https://github.com/bytedance/PXDesign) - Fast, modular, and accurate de novo design of protein binders based on the Protenix foundation model, achieving 17-82% nanomolar hit rates across diverse targets with 2-6× improvement over prior methods like AlphaProteo and RFdiffusion (229+ stars, Apache 2.0)
 - [ODesign (OTeam-AI4S, 2025)](https://github.com/OTeam-AI4S/ODesign) - All-atom generative world model for all-to-all biomolecular interaction design, enabling cross-modality generation of proteins, nucleic acids, small molecules, and cyclic peptides with fine-grained epitope-level control and 2-4 orders of magnitude faster design throughput than modality-specific baselines (316+ stars, Apache 2.0)
+- [OpenDDE (Aureka Research, 2026)](https://github.com/aurekaresearch/OpenDDE) - Open-source, all-atom biomolecular foundation model that turns co-folding into a scalable engine for structure prediction, design, and optimization across proteins, nucleic acids, and small molecules in drug discovery; ranked first on PXMeter-AB, FoldBench-AB, and 2026ARK-AB antibody-antigen benchmarks (263+ stars, Apache 2.0)
 - [La-Proteina (NVIDIA)](https://github.com/NVIDIA-Digital-Bio/la-proteina) - Partially latent flow matching model for the joint generation of a protein's amino acid sequence and full atomistic structure, including both backbone and side chains (2025)
 - [Proteina (NVIDIA, ICLR 2025 Oral)](https://github.com/NVIDIA-Digital-Bio/proteina) - Large-scale flow-based protein backbone generator utilizing hierarchical fold class labels for conditioning with a tailored scalable transformer architecture, enabling controllable de novo protein design (264+ stars)
 - [xfold](https://github.com/Shenggan/xfold) - Democratizing AlphaFold3: PyTorch reimplementation to accelerate protein structure prediction research