Overview
PACCS predicts CCS from molecular conformers using voxel-projected area, molecular-graph information, mass-to-charge ratio and adduct encoding. The Python/notebook workflow includes conformer generation, model training and database-scale prediction.
What can this tool do?
Common uses
- PACCS predicts CCS from molecular conformers using voxel-projected area, molecular-graph information, mass-to-charge ratio and adduct encoding.
Scientific fit
Primary function
Machine learning and prediction
Other functions
Metabolite annotation and identification
Data compatibility
Inputs
Molecular structures
Outputs
Statistical results
Input formats
Molecular SMILES/adduct tables, generated conformers and model weights
Output formats
Predicted CCS tables and model-training artifacts
Scope and considerations
Designed for
- PACCS predicts CCS from molecular conformers using voxel-projected area, molecular-graph information, mass-to-charge ratio and adduct encoding.
Not designed for
- Experimental CCS calibration or an estimate independent of conformer and adduct assumptions.
Important requirements
- Python with RDKit, PyTorch and PyG, SMILES/adduct inputs and a compatible trained model; conformer generation and geometric feature calculation are required preprocessing steps.
Considerations
- Conformer generation, geometry optimization and training-domain coverage influence predictions. Compare with experimental data for the intended ion species and drift-gas conditions.
Access and documentation
BSD-3-Clause source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested.
License: BSD-3-Clause
Evidence and review
Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md.
Core publications
Resource papers, direct updates and tool-specific methods. Selection policy.
- Accurate and Rational Collision Cross Section Prediction Using Voxel‐Projected Area and Deep Learning. Journal of Chemometrics (2025). doi:10.1002/cem.70040
Related tools
Entry history
Changes to this catalogue entry, not the tool's software release history. Acceptance and editorial review are separate.
- 2026-10-27 · First committed entry
Add 45 source-reviewed Enveda tool entries in third expansion batch
View change · Entry at this revision