Menu
Extract explicit safety warnings, GHS classifications, and LD50 profiles from PubChem PUG VIEW.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
| name | chem-hazard-toxicity |
| description | Extract explicit safety warnings, GHS classifications, and LD50 profiles from PubChem PUG VIEW. |
| category | ["chemistry","drug-discovery"] |
To programmatically extract critical safety information from the PubChem PUG-VIEW API. This skill pulls GHS Classifications, Hazard Classes, and Toxicological properties (like LD50/LC50 experimental animal records) for a given compound based on its CID.
Provide the precise CID of the molecule targeting the specific record to query safety metadata. The output is a JSON array of natural language texts sourced from chemical vendors, safety data sheets, and literature.
# Env: base-agent
python .agents/skills/chem-hazard-toxicity/scripts/get_safety_data.py \
--cid 2519 \
--outdir research/caffeine_safety \
--output safety_caffeine.json
We can test the extraction for Caffeine (CID: 2519), a well documented compound.
# Env: base-agent
python .agents/skills/chem-hazard-toxicity/scripts/get_safety_data.py \
--cid 2519 \
--outdir .agents/skills/chem-hazard-toxicity/examples/caffeine \
--output safety_caffeine.json
HTTP 503 blocking via exponential backoff.Author: Bowen Deng Contact: GitHub @learningmatter-mit
Quantify prediction uncertainty of MACE MLIPs using committee (ensemble) models; flag high-uncertainty structures for DFT verification.
Compute phonon-limited carrier mobility and mode-resolved electron-phonon coupling in 2D materials from first principles with Quantum ESPRESSO and EPW.
GPU-accelerated batched inference for MACE, MatGL (TensorNet/M3GNet/CHGNet), and FairChem MLIPs using NValchemi, enabling parallel static, relax, and MD workflows across multiple structures simultaneously.
Extract structured synthesis procedures from a folder of PDFs using the LeMat-Synth GeneralSynthesisOntology schema, producing one JSON file per paper with per-material synthesis records.
Predict LC-MS/MS (MS2, tandem mass spectra) from SMILES via ICEBERG, a two-stage deep neural network. Outputs predicted m/z vs intensity spectrum, fragment ion SMILES, and a spectrum plot.
Match an experimental spectrum (1H NMR, 13C NMR, IR) against predicted or database reference spectra for candidate ranking and structure confirmation. Supports local catalog lookup, public database fallback, and pluggable similarity metrics.