Bond dissociation energy (BDE) is a fundamental thermodynamic parameter that characterizes chemical bond strength and governs the reactivity and thermal behavior of organic molecules. Traditional experimental measurements and high-level quantum chemical calculations are time-consuming and resource-intensive, thus impractical for large-scale molecular screening. Although data-driven machine learning methods have emerged as efficient alternatives, existing approaches rely predominantly on single-modal representations such as molecular graphs or fingerprints, overlooking the chemical semantics embedded in natural language descriptions of bond types, functional group environments, and fragment structures. Here we propose a multimodal fusion framework that integrates molecular structure sequences with chemical natural language text for BDE prediction. The framework comprises three modules: a pre-trained molecular encoder (ChemBERTa or MolFormer) extracting structural features from SMILES, a large language model (DeepSeek-7B, Qwen2-7B or Mistral-7B) encoding chemical text semantics, and a cross-attention-based fusion layer that enables adaptive interaction between them. Low-Rank Adaptation (LoRA) fine-tuning updated only approximately 0.1% of the parameters. Six model combinations were evaluated on a dataset of 531244 BDE records from 65540 organic molecules; the optimal ChemBERTa+Qwen2-7B combination reached a mean absolute error (MAE) of 0.98 kcal mol-1 and a coefficient of determination (R2) of 0.9830 on the validation set. On the publicly released split, the model attains an MAE of 0.98 kcal mol-1 on the official test set, still above the 0.61-0.64 kcal mol-1 reported for bond-centred graph neural networks, with the gap concentrated on heteroatom-containing polar bonds. Scale ablation (Qwen2-0.5B/1.5B/7B: 1.15/1.04/0.98 kcal mol-1) and an output-head ablation (regression head 1.39 vs. generative 1.22 kcal mol-1) show that the fusion of molecular representations, not the output-head form, governs accuracy. The framework thus exploits the complementary strengths of molecular topology and chemical semantics, offering a general route to large-scale BDE prediction without three-dimensional geometries or quantum-chemical features.

Key words: Bond dissociation energy, Large language model, Multiscale fusion, Molecular encoder, Low-rank adaptation, Cross-attention