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

Reviewed-on: #37

upstream/ai-boost-awesome-ai-for-science/catalogue/README.md

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 title: "Readme"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/4afd72ec/README.md
-upstream_sha: 4afd72ec
-imported_at: 2026-07-07
+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
 upstream_changes: accepted
 author: upstream
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 - [ScholarAIO](https://github.com/ZimoLiao/scholaraio) - Agent-agnostic research infrastructure providing AI agents with a structured scientific workspace for deep PDF parsing, hybrid semantic/keyword literature search, citation-graph analysis, topic discovery, and academic writing workflows; natively integrates with Claude Code, Codex, Cursor, Cline, and AgentSkills.io (530+ stars, MIT License, 2026)
 - [BioMCP](https://github.com/genomoncology/biomcp) - Biomedical Model Context Protocol (MCP) server unifying literature search across PubMed/Europe PMC, entity pivoting across genes/variants/drugs/diseases/pathways/proteins, local study analytics, and Claude Code/Codex integration for agentic biomedical research (531+ stars, MIT License, 2025-2026)
 - [MATLAB Agentic Toolkit](https://github.com/matlab/matlab-agentic-toolkit) - Official MathWorks toolkit connecting AI agents to MATLAB via the MATLAB MCP Server and curated skills, enabling trusted engineering and scientific computing workflows with idiomatic code generation, testing, and error diagnosis in Claude Code, GitHub Copilot, OpenAI Codex, and Gemini CLI (686+ stars, BSD-3-Clause, 2026)
+- [BioNeMo Agent Toolkit (NVIDIA)](https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit) - Turn any AI agent into a life science expert with NVIDIA BioNeMo skills, enabling agentic workflows for drug discovery, protein engineering, and biomolecular design (329+ stars, Apache 2.0 / CC-BY-4.0, 2026)
 - [open-science](https://github.com/ai4s-research/open-science) - Local-first, open-source AI workbench for scientists — an open alternative to Claude Science (by ai4s-research, maintainers of this list; TypeScript, MIT, 2026)
+- [OpenScience (Synthetic Sciences)](https://github.com/synthetic-sciences/openscience) - Open-source AI workbench for scientific research that automates the full research loop — literature review, hypothesis generation, code writing, experiment execution, database querying, and report writing — with 290+ skills, specialized research agents, and a browser-based workspace (1453+ stars, Apache 2.0, 2026)
 
 ### Literature Management Plugins
 - [llm-for-zotero](https://github.com/yilewang/llm-for-zotero) - Research agent system deeply integrated with Zotero supporting Agent Mode, skills, multi-model backends (OpenAI-compatible, Claude Code, WebChat, Codex), and MinerU PDF parsing for literature Q&A, summarization, figure inspection, and source comparison (1.3K+ stars, 2026)
@@ -490,6 +492,7 @@ validated: false
 - [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