English(EN) Pyrite: Building and Benchmarking Protein-Ligand Pose Ranking Agents

新的Pyrite基准和LLM代理推动蛋白质-配体结合模式排序研究

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员开发了Pyrite，这是一个用于评估药物发现中蛋白质-配体结合模式排序的新基准和框架。该基准包含1,500个结合模式对，框架包含两个基于LLM的代理：PyriteReasoner用于直接文本推理，PyriteFeaturizer用于生成代码以构建逻辑回归的特征。这些代理的准确率高达74%，超过了人类专家的表现，其中LLM引导的特征发现被证明特别有效。Group Relative Policy Optimization (GRPO)的使用进一步增强了开放权重模型，使其能够媲美甚至超越闭源前沿模型。 AI

影响为LLM在科学推理和药物发现应用特征发现方面的能力建立了一个新基准。

排序理由该集群描述了一篇介绍特定科学任务基准和代理的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Megha Srivastava, Michael H. Lim, Allison M. Keys, Pranav Murugan, Nate Gruver · 2026-11-11 05:00

Pyrite：构建和基准测试蛋白质-配体姿态排序代理

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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报道来源 [1]

Pyrite：构建和基准测试蛋白质-配体姿态排序代理

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