Predict 11B NMR chemical shifts from SMILES using local CPU inference.
npx skills add https://github.com/InternScience/ChemClaw --skill boron-nmr-predict
Please help me install this Agent Skill. Skill: boron-nmr-predict Source: https://github.com/InternScience/ChemClaw/tree/main/skills/boron-nmr-predict Command: npx skills add https://github.com/InternScience/ChemClaw --skill boron-nmr-predict
Boron-containing molecules often require experimental data or expensive quantum calculations to assign 11B NMR shifts. This skill provides an offline, CPU-based workflow to predict boron-11 chemical shifts directly from input SMILES and present a clear, labeled visualization.
Create or activate a CPU-based environment, then run predict with a boron-containing SMILES to obtain per-atom ppm predictions and a labeled image.
High-intent search queries and answers about installing and using this skill.
To predict 11B NMR chemical shifts, provide a boron-containing molecule SMILES to run local CPU inference, which outputs per-atom ppm values and a labeled molecule image. Model weights download automatically from Hugging Face on first use.
Yes, you can predict boron-11 NMR shifts offline using a local 5-fold ensemble model. It avoids expensive quantum calculations by running entirely on CPU inference after the initial Hugging Face model weights download.
No GPU is required; 11B NMR prediction runs entirely on CPU inference. The workflow supports conda-based environment setup to manage dependencies like rdkit-pypi and Pillow for generating labeled molecule images.
The best way to visualize predicted NMR ppm values is by generating a labeled molecule image. The workflow maps chemical shift predictions directly to boron atoms using RDKit and outputs the image file path for review.
The boron NMR prediction model includes solvent handling and optional ML features to enhance prediction accuracy. This approach refines the chemical shift estimates from the 5-fold ensemble based on the molecular environment.
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