English(EN) Active Learning Enables Generation of Molecules that Advance the Known Pareto Front

主动学习流水线生成超越训练分布的新型分子

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-17 04:00

研究人员开发了一个闭环分子生成流水线，该流水线利用量子化学模拟数据进行迭代再训练，以克服当前生成模型的局限性。这种方法能够创建具有超出原始训练分布特性的可合成分子，在分布外分子分类准确性方面取得了显著提高。与静态模型相比，该方法还大大增加了生成稳定、可能可合成分子的比例。 AI

影响这项研究通过改进新型、可合成分子的生成，推动了生成式AI在药物发现和材料科学领域的应用。

排序理由该集群包含一篇详细介绍新型分子生成方法的 ist 研究论文。[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) · Evan R. Antoniuk, Peggy Li, Nathan Keilbart, Stephen Weitzner, Bhavya Kailkhura, Anna M. Hiszpanski · 2026-09-17 04:00

主动学习助力生成突破已知帕累托前沿的分子

arXiv:2501.02059v2 Announce Type: replace Abstract: Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the properties of the structures represented in the traini…