使用GRACE预测碰撞截面：基于早期融合的几何残差加合物条件化
Predicting Collision Cross Sections with GRACE: Geometric Residual Adduct Conditioning via Early-fusion
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中文总结 AI 辅助
GRACE通过早期融合的几何残差加合物条件化，利用预训练几何编码器预测碰撞截面，在多个数据集上取得最优精度，证明残差学习与编码器级条件化的有效性。
中文摘要 AI 辅助
碰撞截面（CCS）源自离子迁移谱，是分子注释的常用描述符。由于它反映气相分子离子的尺寸、形状和电离状态，机器学习模型的预测具有挑战性。大多数预测器要么忽略显式的3D结构，要么将加合物身份视为后期分类特征，这限制了它们捕获加合物依赖性几何效应的能力。我们提出了GRACE（基于早期融合的几何残差加合物条件化），一种3D CCS预测器，通过早期融合的几何残差加合物条件化来适配预训练的分子几何编码器。GRACE结合了两种归纳偏置：相对于加合物感知的物理描述符基线的残差目标，以及通过学习的加合物令牌和低秩注意力适配器在编码器内的加合物条件化。我们在一个精选的包含超过9000条实验分子-加合物CCS记录的数据集上评估了该模型，该数据集包含随机、骨架和加合物敏感划分，旨在分离插值、骨架泛化和加合物驱动的泛化。GRACE在所有三个划分上均达到了评估的学习模型中的最佳平均百分比差异：随机划分为1.67%，骨架划分为2.11%，加合物敏感划分为2.36%。诊断分析表明，残差学习通过去除主导的质量-CCS趋势来稳定训练，而早期融合相对于后期融合改善了加合物敏感的预测。在四个独立的外部测试集上，GRACE显示出比其他评估模型持续更低的误差。在一个留出集上，与四种先前报道的基于物理的工作流程相比，GRACE也达到了最低的平均百分比差异。这些结果支持残差学习和编码器级加合物条件化作为快速、准确CCS预测的实用归纳偏置。
英文摘要
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 state of a gas-phase molecular ion. Most predictors either ignore explicit 3D structure or treat adduct identity as a late categorical feature, which limits their ability to capture adduct-dependent geometric effects. We present GRACE (Geometric Residual Adduct Conditioning via Early-fusion), a 3D CCS predictor that adapts a pretrained molecular geometry encoder using geometric residual adduct conditioning via early fusion. GRACE combines two inductive biases: a residual objective relative to an adduct-aware physical descriptor baseline and adduct conditioning within the encoder via a learned adduct token and low-rank attention adapters. We evaluate the model on a curated set of over 9,000 experimental molecule-adduct CCS records with random, scaffold, and adduct-sensitive splits designed to separate interpolation, scaffold generalization, and adduct-driven generalization. GRACE achieves the best mean percentage difference among the evaluated learned models on all three splits: 1.67% on the random split, 2.11% on the scaffold split, and 2.36% on the adduct-sensitive split. Diagnostic analyses suggest that residual learning stabilizes training by removing the dominant mass-CCS trend, while early fusion improves adduct-sensitive prediction relative to late fusion. Across four independent external test sets, GRACE shows consistently lower error than the other evaluated models. On a held-out set, GRACE also attains the lowest mean percent difference when compared with four previously reported physics-based workflows. These results support residual learning and encoder-level adduct conditioning as practical inductive biases for fast, accurate CCS prediction.
发表机构
- IBM Research（IBM研究院）
- Center for Computational Life Sciences, Cleveland Clinic（克利夫兰诊所计算生命科学中心）
- Department of Chemistry, Michigan State University（密歇根州立大学化学系）
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