Large Language Model-Empowered Compound Collision Cross-Section Prediction

Collision cross section (CCS) is a crucial parameter in ion mobility-mass spectrometry, which plays a significant role in enhancing the precision of compound annotation. Computational prediction methods aim to infer the CCS value from molecular structure and have become a common strategy for efficiently building large-scale CCS compound databases. However, most of the current available methods deliver suboptimal predictive performance due to limited high-quality training data sets and inadequate model architectures for handling multimodal features. To address these issues, we present HyperCCS, a novel CCS prediction framework powered by chemical large language models (CLLMs). Through fine-tuning a CLLM that has been trained on billions of SMILES sequences, HyperCCS effectively captures complex structural and chemical information that the current models trained on a limited CCS data set might miss. A cross-modal feature fusion module is designed to dynamically integrate CLLM-derived features with other heterogeneous features, effectively resolving structural ambiguities commonly found in multimodal features. Benchmark evaluation on the METLIN-CCS and AllCCS2 data sets shows that HyperCCS achieves robust CCS prediction on molecules of various masses, adduct types, and ion modes, outperforming other methods. To showcase the adaptability of HyperCCS within a real-world system, results on in-house experimental data demonstrate its ability to accurately resolve isomers and extrapolate to high-mass analytes. In addition, SHAP analysis and ablation studies confirm the crucial role of CLLM-derived features and the cross-modal feature fusion mechanism in enhancing CCS prediction. HyperCCS is anticipated to offer a high-throughput and cross-instrument computational tool to aid experimental efforts in metabolomics and structural biology.