[upstream-sync] README.md from ai-boost/awesome-ai-for-science@21adf141 [catalogue]

2026-07-09 20:27:36 +00:00
1 changed files with 5 additions and 3 deletions
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 title: "Readme"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/408a2a18/README.md
-upstream_sha: 408a2a18
-imported_at: 2026-07-08
+upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/21adf141/README.md
+upstream_sha: 21adf141
+imported_at: 2026-07-09
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
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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
@@ -532,6 +533,7 @@ validated: false
 - [DeepMol](https://github.com/BioSystemsUM/DeepMol) - Unified ML/DL framework for drug discovery workflows, integrating RDKit, DeepChem, and scikit-learn with SHAP explainability
 - [Chemprop](https://github.com/chemprop/chemprop) - Message passing neural networks for molecule property prediction, ADMET modeling, and reaction prediction, achieving SOTA on MoleculeNet and widely used in pharmaceutical drug discovery (MIT, 2.3K+ stars)
 - [RDKit](https://github.com/rdkit/rdkit) - Cheminformatics toolkit
+- [nvMolKit (NVIDIA BioNeMo, 2025)](https://github.com/NVIDIA-BioNeMo/nvMolKit) - High-performance, GPU-accelerated library for key computational chemistry tasks including molecular similarity, conformer generation, and geometry relaxation, designed to accelerate drug-discovery and molecular-modeling workflows (264+ stars, Apache 2.0)
 - [Open Targets](https://www.opentargets.org/) - Open-source data integration platform for systematic drug target identification and prioritization, combining genetics, genomics, chemistry, and pharmacology data from EMBL-EBI, Wellcome Sanger Institute, and pharmaceutical partners to accelerate therapeutic discovery
 - [ESM3](https://github.com/evolutionaryscale/esm) - 98B-parameter frontier generative model jointly reasoning over protein sequence, structure, and function, trained on 2.78 billion proteins; generated a novel fluorescent protein (esmGFP) with only 58% sequence identity to known GFPs (EvolutionaryScale, 2024)
 - [ProtTrans](https://github.com/agemagician/ProtTrans) - State-of-the-art pretrained language models for proteins trained on thousands of GPUs and Google TPUs using Transformer architectures, enabling protein property prediction, feature extraction, and transfer learning across diverse downstream tasks (1.3K+ stars, MIT, 2020-2026)