The mission of QSAR DataBank (QsarDB) is to provide a platform for organization and long term preservation of QSAR data and models. QsarDB addresses various application and research fields of QSAR community (in Chemical Sciences, Medical and Health Sciences, Biological Sciences, Environmental Sciences, Agricultural and Veterinary Sciences, Materials Sciences, Information and Computing Sciences, Mathematical Sciences).

What is QsarDB?

QsarDB provides open domain-specific digital data exchange standards and associated tools that enable research groups, project teams and institutions to share and represent (Quantitative) Structure-Activity Relationships ((Q)SAR) data and models:

  • The QsarDB repository is a practical resource and tool for the QSAR community.
  • The QsarDB repository is designed for models produced with all statistical and mathematical algorithms that qualitatively or quantitatively express the relationship between the chemical structure and the responses of a compound or material. These responses can belong any of the wide group of endpoints:
    • Physical and chemical properties
    • Ecotoxicity endpoints
    • Environmental fate endpoints
    • Human health endpoints
    • Toxicokinetics endpoints
    • etc.
  • The QsarDB repository advances QSAR best practices (e.g. collecting, systematizing, and reporting data), thereby reducing the time to decision.
  • The QsarDB repository aims to make the processes and outcomes of in silico modelling work transparent, reproducible and accessible.
  • The QsarDB repository follows QSAR reporting guidelines as recommended by OECD and EU REACH regulations
  • The models are represented in the QsarDB data format and stored in a content-aware repository as fully functional is silico models.
  • QSAR information can also be uploaded from QSAR Model Reporting Format (QMRF) documents to make general information about models available.
  • The repository in addition to browsing and downloading models also offers integrated services, such as model analysis and visualization and making predictions.
  • The QsarDB repository unlocks the potential of descriptive and predictive in silico (Q)SAR models by allowing new and different types of collaboration between model developers and model users.
  • QsarDB makes in silico (Q)SAR models citable via unique and persistent identifiers (HDL and DOI).
  • QsarDB makes data and models FAIR (Findable, Accessible, Interoperable, Re-usable).