Vib2Mol: from vibrational spectra to molecular structures-a unified deep learning framework

Key points

• Problem: In closed-loop experiments without humans, the measured spectrum has to be turned into a molecular structure before the next experiment can be chosen. Most existing deep learning methods do either database retrieval or generation; retrieval faces severe limitations for compounds outside the database because experimental spectrum-structure pairs are far fewer than chemical space, and generation is hard because one spectroscopic technique shows only part of the structure of a molecule. The authors argue that the two approaches could complement each other, but this link has gone unused.
• Input and output: The input is an infrared (IR) or Raman spectrum (or both) unified to 1024 points, optionally with a chemical formula, a partially known structure or a database of candidate molecules. The output is a molecular structure written as SMILES (a one-line text notation for molecular structures), and one model covers four tasks: spectrum-to-spectrum retrieval, spectrum-to-structure retrieval, conditional generation that fills in part of a structure, and de novo generation of a whole structure. This is a method paper that proposes a model and evaluates it on benchmarks built from public data and spectra the authors computed.
• Method: An encoder-decoder Transformer is trained in an alignment phase and a generation phase. The alignment phase uses contrastive learning to pull together the spectral and structural embeddings of the same molecule and trains a matching loss on very similar hard negative pairs, and the generation phase jointly trains masked language modeling (MLM), which masks 45% of SMILES tokens and recovers them (conditional generation), and language modeling (LM), which predicts the next token from left to right (de novo generation), with a shared decoder. At inference, coarse-to-fine retrieval finds candidates by embedding similarity and reranks them with the matching module, and generate-then-rerank filters beam search candidates by chemical formula when one is given and then picks among them with the matching module.
• Implementation: A convolution of size 8 slices each spectrum into 128 patches, and patches and SMILES tokens are mapped to 768-dimensional vectors and fed to a 6-layer Transformer encoder. Evaluation uses 662,668 theoretical and 17,774 experimental spectra from three theoretical benchmarks (QM9S, VB-Mols, VB-GEOM) and two experimental ones (SDBS, NIST-IR), and the ViBench spectra built by the authors come, unless otherwise specified, from density functional theory (DFT) calculations in the Gaussian 09 program. Per the official README, the GitHub repository holds the training code and inference scripts, the ViBench dataset and checkpoints are on Hugging Face, and the README recommends Vib2Mol-MMM, one model that takes IR, Raman or IR+Raman, as the default while keeping the modality-specific checkpoints for reproducing the paper.
• Results: With the chemical formula added, spectrum-to-structure retrieval Recall@1 (the share of cases where the top candidate is correct) was 98.11% for Raman and 96.63% for IR on QM9S, on par with SMEN (97.89%, 97.04%), which takes molecular conformations as extra input, and on experimental benchmarks it reached 83.54% on NIST-IR, 86.17% on SDBS-IR and 90.43% on SDBS-Raman, above vibraCLIP. De novo generation with the formula beat the second-best model by at least 10% on QM9S and VB-Mols (86.74% on VB-Mols Raman), and when the multimodal variant Vib2Mol-MM searched 80,368,968 PubChem molecules plus SDBS with SDBS experimental spectra as queries, Recall@1 was 30.60%. On computed Raman spectra that mix reactants and products, the base model fell to 74.29% for retrieval and 36.81% for generation, while Vib2Mol-RXN, trained for mixed spectra, reached 97.51% and 56.64%.
• Limits: Performance drops sharply from theoretical to experimental spectra (the authors cite varying measurement conditions, missing spectral ranges and small sample sizes), and in 5-fold validation on 29 experimental peptide Raman samples, average de novo Recall@1 was 13.34%. In polycyclic aromatic hydrocarbon (PAH) substituent prediction, conditional generation reached a weighted average Recall@1 of 98.95%, but the authors note that this accuracy may be inflated because the '<mask>' markers reveal how many characters to fill. The SMILES-based molecular encoder narrows use on non-molecular systems such as polymers and minerals, and the reaction mixture spectra were simulated by mixing computed spectra by yield while ignoring differences in Raman scattering cross-sections.

Paper links

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