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Predicting Collision Cross Sections with GRACE: Geometric Residual Adduct Conditioning via Early-fusion — arXiv:2609.12223

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.12223
License (as archived): CC0-1.0
Archived: 2026-10-16T03:03:12.432Z
SHA-256 of the archived text: 25c80c9b387f3e9128538bbe3a1caca1029880b8c0d68009d00fb91498417b33
Stable URL of this page: https://metraipolis.com/documento/dc_2d41bf22eef6

How to cite

Predicting Collision Cross Sections with GRACE: Geometric Residual Adduct Conditioning via Early-fusion — arXiv:2609.12223 — arXiv (en), https://arxiv.org/abs/2609.12223 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2d41bf22eef6 (Metraipolis archive dc_2d41bf22eef6, sha256 25c80c9b387f3e9128538bbe3a1caca1029880b8c0d68009d00fb91498417b33)

BibTeX

@misc{dc2d41bf22eef6,
  title = {Predicting Collision Cross Sections with GRACE: Geometric Residual Adduct Conditioning via Early-fusion — arXiv:2609.12223},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.12223},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2d41bf22eef6; sha256 25c80c9b387f3e9128538bbe3a1caca1029880b8c0d68009d00fb91498417b33},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.12223. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-14 in cs.AI).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM).
Pagina del lavoro: https://arxiv.org/abs/2609.12223 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T02:51:27.877Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).