{"slug": "pyrite-building-and-benchmarking-protein-ligand-pose-ranking-agents", "title": "Pyrite: Building and Benchmarking Protein-Ligand Pose Ranking Agents", "summary": "Researchers submitted Pyrite, a pairwise protein-ligand pose-ranking benchmark of 1,500 pose pairs, to arXiv on 10 Nov 2026, reporting that two LLM-based agents built on a structured representation called PyriteRep beat expert chemists on the task. PyriteFeaturizer, which generates executable code to build features for logistic regression, reached 74% accuracy, and PyriteReasoner, which compares poses through text-based reasoning, reached 67%, while expert chemists using 3D visualization tools and physics-based scores performed near chance. Group Relative Policy Optimization (GRPO) improved open-weight models enough to match or surpass closed-source frontier models in the evaluations, and LLM-guided feature discovery generally outperformed direct reasoning.", "body_md": "# Computer Science > Machine Learning\n\n  [Submitted on 10 Nov 2026]\n\n# Title:Pyrite: Building and Benchmarking Protein-Ligand Pose Ranking Agents\n\n[View PDF](http://arxiv.org/pdf/2611.14511v1)\n\n[HTML (experimental)](https://arxiv.org/html/2611.14511v1)\n\nAbstract:Predicting how a ligand binds to its target is central to small-molecule drug discovery. Modern cofolding models can generate diverse candidate poses, but reliably ranking these candidates remains challenging. We introduce Pyrite, a pairwise pose-ranking benchmark comprising 1,500 pose pairs across diverse protein-ligand complexes. Expert chemists equipped with 3D visualization tools and physics-based scores perform near chance, highlighting the difficulty of interpreting available chemical signals. To investigate automated ranking, we introduce PyriteRep, a structured representation of molecular geometry, protein-ligand interactions, and physicochemical scores, and develop two LLM-based agents: PyriteReasoner, which directly compares poses through text-based reasoning, and PyriteFeaturizer, which generates executable code to construct features for logistic regression, optionally incorporating features derived from LLM subcalls. These agents achieve up to 67% and 74% accuracy, respectively, exceeding observed expert performance. LLM-guided feature discovery generally outperforms direct reasoning, while Group Relative Policy Optimization (GRPO) improves open-weight models, enabling them to match or surpass closed-source frontier models in our evaluations. These results establish Pyrite as a challenging test of pose-ranking ability and highlight LLM-guided feature discovery as a promising approach to interpret and combine the outputs of computational chemistry tools.\n    \n\n## Submission history\n\nFrom: Megha Srivastava [\n[view email](http://arxiv.org/show-email/0fe135fa/2611.14511)]\n\n**[v1]** Tue, 10 Nov 2026 18:56:32 UTC (5,909 KB)\n\n### Additional Features\n\n### Current browse context:\n\ncs.LG\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\nIArxiv Recommender\n\n*(*[What is IArxiv?](https://iarxiv.org/about))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/pyrite-building-and-benchmarking-protein-ligand-pose-ranking-agents", "canonical_source": "http://arxiv.org/abs/2611.14511v1", "published_at": "2026-11-11 16:49:50+00:00", "updated_at": "2026-11-11 17:21:08.696478+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "ai-agents", "large-language-models", "artificial-intelligence"], "entities": ["Pyrite", "PyriteRep", "PyriteReasoner", "PyriteFeaturizer", "Group Relative Policy Optimization", "arXiv", "Megha Srivastava"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/pyrite-building-and-benchmarking-protein-ligand-pose-ranking-agents", "markdown": "https://wpnews.pro/news/pyrite-building-and-benchmarking-protein-ligand-pose-ranking-agents.md", "text": "https://wpnews.pro/news/pyrite-building-and-benchmarking-protein-ligand-pose-ranking-agents.txt", "jsonld": "https://wpnews.pro/news/pyrite-building-and-benchmarking-protein-ligand-pose-ranking-agents.jsonld"}}