English(EN) Uncertainty-aware reinforcement learning for chemical language models

不确定性感知强化学习增强化学语言模型用于药物设计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-25 04:00

研究人员开发了将预测不确定性纳入化学语言模型（CLMs）强化学习的新方法。这些方法旨在通过引导CLMs避开化学空间的不确定区域来改进分子的从头设计。通过将不确定性视为优化目标或用于调节策略更新，模型可以实现更可靠的命中发现，提高真实命中率和真实命中总数。 AI

影响增强了人工智能驱动的分子设计的可靠性，可能通过关注更确定的预测来加速药物发现。

排序理由该集群包含一篇详细介绍改进AI模型的新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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

本文如何被排名

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arXiv cs.LG TIER_1 English(EN) · Borja Medina, Jon Paul Janet · 2026-06-25 04:00

面向化学语言模型的“不确定性感知”强化学习

arXiv:2606.24990v1 Announce Type: new Abstract: Reinforcement Learning (RL) has become a powerful paradigm for de novo molecular design, enabling Chemical Language Models (CLMs) to navigate and explore the chemical space while optimizing specific desired properties. However, the …