CAS Common Chemistry

CAS Common Chemistry provides open-access chemical substance information, including identifiers and computer-readable chemical structures for 500,000 substances, to support chemical informatics and related research.


Key Features:

  • Extensive Dataset: Contains records for 500,000 chemical substances with basic properties and computer-readable chemical structure information.
  • Enhanced Search Capabilities: Improved search functionalities support locating specific chemical substances and associated data.
  • Integrated API: Provides an API for programmatic access and integration of CAS Common Chemistry data into external systems.
  • Reusable Licensing: Content is available under the Creative Commons Attribution-Non-Commercial (CC-BY-NC 4.0) license.

Scientific Applications:

  • Medicinal chemistry: Supports drug discovery research by providing substance identifiers and structures for compound characterization.
  • Environmental science: Facilitates toxicological assessments and environmental chemistry analyses using standardized substance information.
  • Materials science: Enables material development workflows that require validated chemical substance data and structures.
  • Chemical informatics: Supports computational modeling, cheminformatics analyses, and integration with other chemical data resources.

Methodology:

Curating and maintaining an extensive database of chemical substances and integrating computer-readable chemical structure information to enable computational analyses and simulations.

Topics

Details

License:
CC-BY-NC-4.0
Cost:
Free of charge (with restrictions)
Tool Type:
web application
Operating Systems:
Mac, Linux, Windows
Added:
8/11/2022
Last Updated:
11/24/2024

Operations

Publications

Jacobs A, Williams D, Hickey K, Patrick N, Williams AJ, Chalk S, McEwen L, Willighagen E, Walker M, Bolton E, Sinclair G, Sanford A. CAS Common Chemistry in 2021: Expanding Access to Trusted Chemical Information for the Scientific Community. Journal of Chemical Information and Modeling. 2022;62(11):2737-2743. doi:10.1021/acs.jcim.2c00268. PMID:35559614. PMCID:PMC9199008.