# Pyrite: Building and Benchmarking Protein-Ligand Pose Ranking Agents

> Source: <http://arxiv.org/abs/2611.14511v1>
> Published: 2026-11-11 16:49:50+00:00

# Computer Science > Machine Learning

  [Submitted on 10 Nov 2026]

# Title:Pyrite: Building and Benchmarking Protein-Ligand Pose Ranking Agents

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Abstract: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.
    

## Submission history

From: Megha Srivastava [
[view email](http://arxiv.org/show-email/0fe135fa/2611.14511)]

**[v1]** Tue, 10 Nov 2026 18:56:32 UTC (5,909 KB)

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