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New Pyrite Benchmark and LLM Agents Advance Protein-Ligand Pose Ranking

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Pyrite, a new benchmark and framework for evaluating protein-ligand pose ranking in drug discovery. The benchmark includes 1,500 pose pairs, and the framework features two LLM-based agents: PyriteReasoner for direct text-based reasoning and PyriteFeaturizer for generating code to construct features for logistic regression. These agents achieve up to 74% accuracy, surpassing human expert performance, with LLM-guided feature discovery proving particularly effective. The use of Group Relative Policy Optimization (GRPO) further enhances open-weight models to match or exceed closed-source frontier models. AI

IMPACT Establishes a new benchmark for LLM capabilities in scientific reasoning and feature discovery for drug discovery applications.

RANK_REASON The cluster describes a new academic paper introducing a benchmark and agents for a specific scientific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Pyrite Benchmark and LLM Agents Advance Protein-Ligand Pose Ranking

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The cluster describes a new academic paper introducing a benchmark and agents for a specific scientific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Megha Srivastava, Michael H. Lim, Allison M. Keys, Pranav Murugan, Nate Gruver ·

    Pyrite: Building and Benchmarking Protein-Ligand Pose Ranking Agents

    arXiv:2611.14511v1 Announce Type: new 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…

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