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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
Learn more - or try it out yourself : Wir offer a free version of OCHEM via the Federal Helmholtz Research Center in Munich


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).
  • Application of the self-learning feature of our software can decrease the error further from 1.02 log units to 0.59 log units (Tetko et al, 2009). (exemplaric on a test set of 95809 Pfizer-Molek├╝len)
  • Moreover, our applicability domain approach allows to identify the 60% of the structures for which the predicition accuray reaches 0.33 log units.
  • ALOGPS 3.01 was developed using the same approach, Associative Neural Networks, but on a 50% larger dataset of molecules for logP and almost 8 times larger dataset for water solubility.
  • We are currently developing new modules for the prediction of DMSO solubility and pKa prediction, other important parameters in early drug research.