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)
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
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.