Chemistry

Provides comprehensive API access to the ZINC database, a UCSF-maintained repository of 230M+ purchasable chemical compounds. Supports searching by ZINC ID or SMILES notation, similarity searches, random sampling, and downloading 3D structures for molecular docking studies in drug discovery workflows.

This skill enables querying the PubChem database, the world's largest freely available chemical database with 110M+ compounds. It supports searching compounds by name, structure (SMILES/InChI), or formula, retrieving molecular properties, performing similarity and substructure searches, and accessing bioactivity data from screening assays.

This skill provides comprehensive documentation for accessing the NIH Metabolomics Workbench REST API, enabling queries for metabolite structures, study metadata, RefMet nomenclature standardization, mass spectrometry m/z searches, and gene/protein associations across 4,200+ metabolomics studies.

This skill provides comprehensive guidance for accessing the Human Metabolome Database (HMDB), a freely available resource containing detailed information about 220,000+ human metabolites. It covers web-based searches, spectral matching, data downloads, and common research workflows for metabolomics, biomarker discovery, and metabolite identification.

RDKit is a comprehensive cheminformatics skill that guides users through molecular analysis tasks including reading/writing chemical structures (SMILES, SDF), calculating molecular descriptors (LogP, TPSA, molecular weight), generating fingerprints for similarity searching, and performing substructure matching. It includes ready-to-use Python scripts for drug-likeness screening, similarity searches, and functional group filtering.

Pymatgen is a comprehensive Python library for materials analysis that enables working with crystal structures, computing phase diagrams, analyzing electronic structure data, and accessing the Materials Project database. It supports 100+ file formats and integrates with major computational chemistry codes like VASP, Gaussian, and Quantum ESPRESSO.

This skill provides comprehensive guidance for using the matchms Python library for mass spectrometry data processing and analysis. It covers spectral similarity calculations, compound identification from spectral libraries, data filtering, and format conversion for metabolomics research workflows.

A comprehensive molecular machine learning skill using DeepChem for predicting chemical properties like solubility and toxicity. It supports graph neural networks, transfer learning with pretrained models (ChemBERTa, GROVER), and MoleculeNet benchmarks for drug discovery and materials science applications.

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

Access and analyze comprehensive drug information from the DrugBank database including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. This skill should be used when working with pharmaceutical data, drug discovery research, pharmacology studies, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task requiring detailed drug and drug target information from DrugBank.