Predict per-atom 1H and 13C NMR chemical shifts from SMILES strings.
npx skills add https://github.com/InternScience/ChemClaw --skill nmr-prediction
Please help me install this Agent Skill. Skill: nmr-prediction Source: https://github.com/InternScience/ChemClaw/tree/main/skills/nmr-prediction Command: npx skills add https://github.com/InternScience/ChemClaw --skill nmr-prediction
Predicts per-atom ¹H and ¹³C NMR chemical shifts and Lorentzian-broadened spectra from a SMILES string, enabling quick, interpretation-friendly insights without demanding quantum-chemical calculations.
Run python nmr_prediction.py 'CCO' to predict 1H/13C shifts and generate the spectrum image.
High-intent search queries and answers about installing and using this skill.
To predict NMR chemical shifts from a SMILES string, provide the SMILES input to run NMRNet inference, which outputs per-atom 1H and 13C ppm values and renders spectrum PNGs without requiring quantum-chemical calculations.
You can predict NMR spectra for small- to medium-sized organic molecules. The Skill applies NMRNet inference to these molecule sizes to generate per-atom chemical shifts and Lorentzian-broadened spectrum visualizations.
Using deep-learning based inference with NMRNet is the best way to get NMR-like insights without DFT. It rapidly predicts 1H and 13C chemical shifts and generates visual spectra from SMILES strings.
NMR shifts are visualized by generating Lorentzian-broadened spectrum PNGs. The Skill outputs these images alongside per-atom 1H and 13C ppm values after processing the input SMILES string.
NMR prediction requires pre-trained NMRNet weights and the Uni-Core runtime. These dependencies are necessary to perform the deep-learning inference and render the resulting NMR spectra.
The limitation of using deep learning for NMR shift prediction is that it applies only to small- to medium-sized organic molecules, providing rapid screening insights rather than the exact accuracy of heavy quantum-chemical calculations.
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