Geometric model predicts molecule shapes and sizes more accurately
Predicting Collision Cross Sections with GRACE: Geometric Residual Adduct Conditioning via Early-fusion
Machine LearningArtificial Intelligence
Summary
Knowing how molecules travel through gas helps scientists identify them, but predicting this requires understanding their 3D shape and how they carry extra bits called adducts. The authors created a model called GRACE that learns from both the molecule's shape and details about these adducts early in its process, improving the predictions. Tested on thousands of examples, GRACE was more accurate than past models, especially when dealing with new or unusual molecules. This could help researchers analyze molecules faster and more reliably.
What this means in practice
- •For pharmaceutical chemists: Predict molecule sizes and shapes in gas phase to speed up drug identification using ion mobility data analysis.
- •For chemical instrumentation teams: Enhance ion mobility mass spectrometry software to offer faster, more accurate molecule feature predictions based on 3D data and adduct effects.
Authors
Parthasarathy Suryanarayanan, Susanta Das, Shreyans Sethi, Kenneth M. Merz,, Joseph A. Morrone
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 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.