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English(EN) RetroDFM-R: Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning

新的大语言模型RetroDFM-R提供推理驱动的逆合成预测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了RetroDFM-R，这是一种新开发的大语言模型，专为有机合成和药物发现中的逆合成规划而设计。该模型利用强化学习，不仅提供准确的预测，还提供透明的、分步的化学推理。RetroDFM-R在USPTO-50K基准测试中表现出优越的性能，其准确性高于先前最先进的方法，并在重建复杂的药物合成路线方面显示出潜力。 AI

影响 该模型通过提供更准确和可解释的逆合成路径，有可能加速药物发现和化学合成。

排序理由 该集群包含一篇详细介绍新模型和基准测试结果的arXiv论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的大语言模型RetroDFM-R提供推理驱动的逆合成预测

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该集群包含一篇详细介绍新模型和基准测试结果的arXiv论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Situo Zhang, Hanqi Li, Lu Chen, Zihan Zhao, Xuanze Lin, Zichen Zhu, Danyu Luo, Bo Chen, Xin Chen, Kai Yu ·

    RetroDFM-R：基于强化学习的大语言模型驱动的推理式逆合成预测

    arXiv:2507.17448v2 Announce Type: replace-cross Abstract: Retrosynthetic planning is a cornerstone of organic synthesis and drug discovery. Yet existing AI methods often rely on pattern matching rather than transferable chemical reasoning, limiting both generalizability and inter…

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