English(EN) Predicting Collision Cross Sections with GRACE: Geometric Residual Adduct Conditioning via Early-fusion

新的GRACE模型提高了分子碰撞截面预测精度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员开发了GRACE（Geometric Residual Adduct Conditioning via Early-fusion），一种用于预测分子注释碰撞截面（CCS）的新型机器学习模型。与以往将加合物身份视为后期特征或忽略3D结构的方法不同，GRACE在编码过程早期就整合了加合物信息。这种方法包括残差学习和带有注意力适配器的学习加合物标记，显著提高了各种泛化分割的预测精度，并在外部测试集上优于现有的基于物理的工作流程。 AI

影响提高了分子特性的预测精度，可能加速药物发现和化学分析。

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arXiv cs.LG TIER_1 English(EN) · Parthasarathy Suryanarayanan, Susanta Das, Shreyans Sethi, Kenneth M. Merz, Jr., Joseph A. Morrone · 2026-09-14 04:00

利用GRACE预测碰撞截面：通过早期融合进行的几何残差加合物条件化

arXiv:2609.12223v1 Announce Type: new Abstract: Collision cross section (CCS), derived from ion mobility mass spectrometry, is a common descriptor for molecular annotation. Prediction is challenging for machine learning models because it reflects the size, shape, and ionization s…

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利用GRACE预测碰撞截面：通过早期融合进行的几何残差加合物条件化

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