Molecular Formula Prediction from NMR Spectra
Summary
Predict the molecular formula (elemental composition) of an unknown compound from 1D ¹H and/or ¹³C NMR spectra using a multitask transformer-based machine learning model. This skill enables rapid structure elucidation traditionally performed by manual spectroscopic interpretation.
When to use
Apply this skill when you have 1D ¹H or ¹³C NMR spectra from an unknown organic compound and need to predict its molecular formula (e.g., C₆H₁₂O₂). The compound must contain ≤19 heavy (non-hydrogen) atoms. Use this as the first stage of a structure elucidation pipeline before predicting molecular connectivity.
When NOT to use
- Input compound contains >19 heavy atoms — model was only validated on molecules up to C/H/N/O/S/halide with ≤19 non-hydrogen atoms.
- Input is a 2D NMR spectrum (HSQC, HMBC, COSY) — this skill is trained exclusively on 1D spectra.
- The spectrum is from a mixture or unknown impurities — the model assumes a single pure compound.
Inputs
- 1D ¹H NMR spectrum (token-encoded spectral features or raw peak list)
- 1D ¹³C NMR spectrum (token-encoded spectral features or raw peak list)
- Ground-truth molecular formulas (for training/validation)
Outputs
- Predicted molecular formula (element count vector or SMILES-like formula string)
- Prediction confidence scores (softmax probabilities per element)
- Accuracy metric on held-out test set
How to apply
Encode the input 1D NMR spectrum as a token sequence representing spectroscopic features (chemical shifts, intensities, multiplicities). Feed this sequence through a transformer encoder that learns to map spectral embeddings to a chemical formula space. The model is trained end-to-end using supervised classification loss on ground-truth molecular formulas. At inference, the transformer decoder outputs a predicted molecular formula as a vector encoding element counts (e.g., [C_count, H_count, O_count, N_count, ...]). Validate predictions by comparing predicted formula to ground truth using exact-match accuracy or elemental composition metrics. The model integrates multi-head self-attention to capture long-range spectral correlations and learns which NMR peaks are most diagnostic for formula prediction.
Related tools
- PyTorch (Neural network framework for implementing transformer encoder-decoder architecture, training supervised loss on formula classification, and inference) — https://pytorch.org
- Transformer (encoder-decoder architecture) (Core neural architecture with multi-head self-attention and cross-attention layers to map NMR spectral embeddings to molecular formula space)
Evaluation signals
- Exact-match accuracy: predicted formula matches ground-truth formula (e.g., C₆H₁₂O₂)
- Element-wise accuracy: each predicted element count (C, H, O, N, etc.) matches ground truth
- Schema invariant: output vector has fixed length equal to number of tracked elements and sums to expected molecular weight range
- Held-out test set accuracy reported separately for ¹H-only, ¹³C-only, and combined spectra inputs
- No negative element counts or implausible formulas (e.g., H₀ or C₀)
Limitations
- Validated only on molecules with ≤19 heavy atoms; performance unknown for larger molecules.
- Requires high-quality, well-resolved 1D NMR spectra; noisy, overlapped, or sparse spectra may degrade predictions.
- Model is trained on a specific dataset; generalization to novel chemical scaffolds not in training set is not characterized.
- Does not handle isotope information (e.g., ¹³C labeling) — treats all carbons uniformly.
Evidence
- [intro] A multitask machine learning framework can predict molecular structure (formula and connectivity) from 1D ¹H and/or ¹³C NMR spectra: "we introduce a multitask machine learning framework that predicts the molecular structure (formula and connectivity) of an unknown compound solely based on its 1D ¹H and/or ¹³C NMR spectra"
- [methods] Training uses supervised loss on formula prediction as classification task: "Train the transformer end-to-end using supervised loss on formula prediction (classification) and bond connectivity prediction (link prediction on molecular graph)"
- [methods] Validation involves comparing predicted formula to ground truth: "Validate the trained model on held-out test molecules by comparing predicted formula and connectivity to ground truth, computing accuracy metrics for both outputs"
- [intro] Framework is effective for molecules with up to 19 heavy atoms: "We demonstrate the effectiveness of this framework on molecules with up to 19 heavy (non-hydrogen) atoms"
1---2name: molecular-formula-prediction-23description: Use when you have 1D ¹H or ¹³C NMR spectra from an unknown organic compound and need to predict its molecular formula (e.g., C₆H₁₂O₂). The compound must contain ≤19 heavy (non-hydrogen) atoms. Use this as the first stage of a structure elucidation pipeline before predicting molecular connectivity.4license: CC-BY-4.05---67# Molecular Formula Prediction from NMR Spectra89## Summary1011Predict the molecular formula (elemental composition) of an unknown compound from 1D ¹H and/or ¹³C NMR spectra using a multitask transformer-based machine learning model. This skill enables rapid structure elucidation traditionally performed by manual spectroscopic interpretation.1213## When to use1415Apply this skill when you have 1D ¹H or ¹³C NMR spectra from an unknown organic compound and need to predict its molecular formula (e.g., C₆H₁₂O₂). The compound must contain ≤19 heavy (non-hydrogen) atoms. Use this as the first stage of a structure elucidation pipeline before predicting molecular connectivity.1617## When NOT to use1819- Input compound contains >19 heavy atoms — model was only validated on molecules up to C/H/N/O/S/halide with ≤19 non-hydrogen atoms.20- Input is a 2D NMR spectrum (HSQC, HMBC, COSY) — this skill is trained exclusively on 1D spectra.21- The spectrum is from a mixture or unknown impurities — the model assumes a single pure compound.2223## Inputs2425- 1D ¹H NMR spectrum (token-encoded spectral features or raw peak list)26- 1D ¹³C NMR spectrum (token-encoded spectral features or raw peak list)27- Ground-truth molecular formulas (for training/validation)2829## Outputs3031- Predicted molecular formula (element count vector or SMILES-like formula string)32- Prediction confidence scores (softmax probabilities per element)33- Accuracy metric on held-out test set3435## How to apply3637Encode the input 1D NMR spectrum as a token sequence representing spectroscopic features (chemical shifts, intensities, multiplicities). Feed this sequence through a transformer encoder that learns to map spectral embeddings to a chemical formula space. The model is trained end-to-end using supervised classification loss on ground-truth molecular formulas. At inference, the transformer decoder outputs a predicted molecular formula as a vector encoding element counts (e.g., [C_count, H_count, O_count, N_count, ...]). Validate predictions by comparing predicted formula to ground truth using exact-match accuracy or elemental composition metrics. The model integrates multi-head self-attention to capture long-range spectral correlations and learns which NMR peaks are most diagnostic for formula prediction.3839## Related tools4041- **PyTorch** (Neural network framework for implementing transformer encoder-decoder architecture, training supervised loss on formula classification, and inference) — https://pytorch.org42- **Transformer (encoder-decoder architecture)** (Core neural architecture with multi-head self-attention and cross-attention layers to map NMR spectral embeddings to molecular formula space)4344## Evaluation signals4546- Exact-match accuracy: predicted formula matches ground-truth formula (e.g., C₆H₁₂O₂)47- Element-wise accuracy: each predicted element count (C, H, O, N, etc.) matches ground truth48- Schema invariant: output vector has fixed length equal to number of tracked elements and sums to expected molecular weight range49- Held-out test set accuracy reported separately for ¹H-only, ¹³C-only, and combined spectra inputs50- No negative element counts or implausible formulas (e.g., H₀ or C₀)5152## Limitations5354- Validated only on molecules with ≤19 heavy atoms; performance unknown for larger molecules.55- Requires high-quality, well-resolved 1D NMR spectra; noisy, overlapped, or sparse spectra may degrade predictions.56- Model is trained on a specific dataset; generalization to novel chemical scaffolds not in training set is not characterized.57- Does not handle isotope information (e.g., ¹³C labeling) — treats all carbons uniformly.5859## Evidence6061- [intro] A multitask machine learning framework can predict molecular structure (formula and connectivity) from 1D ¹H and/or ¹³C NMR spectra: "we introduce a multitask machine learning framework that predicts the molecular structure (formula and connectivity) of an unknown compound solely based on its 1D ¹H and/or ¹³C NMR spectra"62- [methods] Training uses supervised loss on formula prediction as classification task: "Train the transformer end-to-end using supervised loss on formula prediction (classification) and bond connectivity prediction (link prediction on molecular graph)"63- [methods] Validation involves comparing predicted formula to ground truth: "Validate the trained model on held-out test molecules by comparing predicted formula and connectivity to ground truth, computing accuracy metrics for both outputs"64- [intro] Framework is effective for molecules with up to 19 heavy atoms: "We demonstrate the effectiveness of this framework on molecules with up to 19 heavy (non-hydrogen) atoms"
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