• Company type:
      • Manufacturer
    • Year founded:
    • not available
    • Employees (at the site):
    • not available
    • Turnover range:
    • not available
    • Products/services:
    • eADMET GmbH develops and markets innovative IT solutions for the prediction of important properties of chemicals and drugs, especially physical and ("drug-like") characteristics.

      OCHEM - our platform to create in silico ADME/Tox prediction models.

      OCHEM makes it easy to create precise models for properties of chemicals.

      Webbased, completely integrated development system for Structure-Property- und Structure-Activity Relationships (QSPR, QSAR)
      Contains a large database of physical und ADME/T properties
      Requires only structural information
      Supports the identification of the mode of action
      Delivers background information for early stages of compound development
      Saves time and costs by allowing to eliminate redundant tests
      Allows distributed modeling in global teams
      Supports a wide variety of well-established machine learning and molecular descriptor programs.
      Associative Neural Networks, Fast Stagewise Multiple Linear Regression, K-Nearest Neighbors, Kernel Ridge Regression, SVM, Partial Least Squares, Random Forests, Decision Trees
      E-state, ALogPS, MolPrint, GSFragment, Dragon, ISIDA, MOPAC, ADRIANA.Code, CDK, QNPR, ShapeSignatures, 'Inductive' descriptors, MERA, MERSY, Vina Docking based descriptors, Chemaxon descriptors, Chiral Descriptors, ETM descriptors, Spectrophores

      ePhysChem - fast and precise estimation of physicochemical and ADME/Tox properties.

      Using ePhysChem, physical and ADME/Tox properties of chemicals can be estimated easily, quickly and precisely.

      ePhysChem contains AlogPS 3.01 for logP- and logS-prediction.
      ALOGPS 2.1 has been tested by various pharma companies and has repeatedly delivered excellent results (see VCCLAB).
      ALOGPS was recently benchmarked as the best off-the shelf package for the prediction of logP values on a set of in-house data from Pfizer & Nycomed - in comparison to 12 commercial and 6 public approaches (Mannhold et al, 2009).

    • Core competencies:
    • not available

    • Language skills:
    • English
    • Key Tech / section:
      • Biotechnology: Information systems for biotechnology
      • Chemical Industry: Information technology for chemical industry
      • Cultural and Creative Industries: Software and games
      • Information Technology: Software for industry
    • Certification:
    • not available

    • Sales markets - target industries:
    • not available
    • Sales markets - target countries:
    • not available
    • Cooperation offers:
    • not available
    • Contact person:
    • Ahmed Abdelaziz (Mr.)
      - Management