新的EW-SFT方法增强了各种AI模型的分子优化能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了一种名为Elite-Weighted Supervised Fine-tuning (EW-SFT) 的新方法，用于目标导向的分子优化。该技术通过使用奖励信号选择得分高的分子，并用其在选定集上的原生预训练损失来更新模型，从而解决了传统强化学习方法的局限性。EW-SFT设计通用性强，可应用于各种分子生成器架构和设计任务，无需复杂的轨迹对数概率。实验表明，EW-SFT在固定预算下始终优于原生优化器，并在样本效率基准测试中提高了效率。 AI

影响引入了一种更统一、更有效的分子优化方法，有望加速药物发现和材料科学的发展。

排序理由详细介绍一种新的分子优化方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Shiyun Wa, Yifei Wang, Anna G. Green, Simone Sciabola, Ye Wang · 2026-09-02 04:00

面向目标导向的分子优化的精英加权监督微调

arXiv:2609.00189v1 Announce Type: new Abstract: Goal-directed optimization is essential for steering molecular generators to propose candidates with desired properties. However, it is often implemented with policy-gradient reinforcement learning, which requires a generation-traje…

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面向目标导向的分子优化的精英加权监督微调

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