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.