Vib2Mol: from vibrational spectra to molecular structures-a versatile deep learning model

Figure 1. The framework of Vib2Mol for pretraining. (A) The alignment phase: spectra and molecular structures are represented as patch tokens and SMILES tokens, respectively. After processed by their encoders, spectral and molecular information are aligned by CL. Subsequently, hard negative samples are selected and employed to guide model in learning the subtle distinctions between these highly similar spectra or molecule samples. (B) The generation phase: for conditional generation, molecules are randomly masked 45% and encoded by the same molecular encoder used for spectrum-structure alignment. The molecular decoder fuses spectral information with molecular features and predicts masked tokens. For de novo generation, molecule is sequentially masked and directed input into the same molecular decoder as conditional generation without the prior encoding. Then, the decoder predicts the next token on the basis of previous information, spectral features and chemical formulae (if given).

Figure 2. The workflow of Vib2Mol for addressing different spectrum-to-structure tasks: (A) spectrum-spectrum retrieval, where only the spectral encoder is used to calculate the similarity between spectral pairs; (B) spectrum-structure retrieval, where spectra and molecules are encoded by their respective encoders to determine spectrum-structure similarity; (C) conditional generation, and (D) de novo generation, both following workflows during the stage of pretraining. (E) re-ranking module for refining retrieval and generation results. It initially filters candidates by chemical formula (if available), then uses a pre-trained molecular encoder to score them against the query spectrum. High-scoring candidates are finally selected as output.

Figure 3. Performance evaluation of advanced deep learning models. (A) and (B) present a performance comparison of various models on spectrum-to-structure retrieval and de novo molecular generation, respectively. These evaluations were conducted on both theoretical (VB, QM9S) and experimental (NIST, SDBS) benchmarks. The impact of multi-modal spectral input on performance of Vib2Mol is further detailed in (C) for retrieval and (D) for generation.

Figure 4. Workflow and performance of Vib2Mol in product elucidation and mixed-spectrum analysis. (A) Three scenarios for predicting products. (B) Benchmarking on PAH substitution reactions. (C) Retrieval and de novo generation results on unmixed and mixed spectra of general chemical reactions.