ClipMol: A Molecular Representation Learning Framework for CCS Prediction via SMILES-InChI Dual-View Chemical Language Alignment.
TL;DR
ClipMol is proposed, a scalable and structurally faithful solution for molecular representation learning and IM-MS-related CCS prediction in analytical chemistry that jointly models local chemical microenvironments and global structural constraints of molecules.
Abstract
Although existing molecular pretraining models have achieved favorable performance on various downstream tasks, their reliance on explicit three-dimensional conformer sampling or external natural-language corpora often increases computational cost and affects structural fidelity. Here, we propose ClipMol, a molecular representation learning framework based on SMILES-InChI dual-view chemical-language alignment. Without requiring explicit three-dimensional conformers or external corpora, ClipMol jointly models local chemical microenvironments and global structural constraints of molecules. Benchmark results show that ClipMol and its scaled variant, ClipMol-XL, achieve strong overall performance on both classification and regression tasks. More importantly, for collision cross-section (CCS) prediction in ion mobility-mass spectrometry, ClipMol shows stable and competitive performance on two independent benchmark data sets, METLIN-CCS and ALLCCS, while maintaining robustness across different adduct compositions and diverse chemical categories. Compared with state-of-the-art and competitive CCS prediction models, the ClipMol models achieved the best or highly competitive averaged performance on both data sets, with ClipMol-XL showing the strongest overall R2 and root-mean-square error performance. Overall, ClipMol provides a scalable and structurally faithful solution for molecular representation learning and IM-MS-related CCS prediction in analytical chemistry.