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Concisely, the main features of the modeling framework within the OCHEM include:

  • Support of regression and classification models
  • Calculation of various molecular descriptors ranging from molecular fragments to quantum chemical descriptors. Both whole-molecule and per-atom descriptors are supported.
  • Tracking of each compound from the training and validation sets
  • Basic and detailed model statistics and evaluation of model performance on training and validation sets
  • Assessment of applicability domain of the models and their prediction accuracy
  • Pre-filtering of descriptors: manual selection, decorrelation filter, principal component analysis (PCA) based selection
  • Various machine learning methods including both linear and non-linear approaches
  • N-fold cross-validation and bagging validation of models
  • Multi-learning: models can predict several properties simultaneously
  • Combining data with different conditions of measurements and the data in different measurement units
  • Distribution of calculations to an internal cluster of Linux and Mac computers
  • Scalability and expendability for new descriptors and machine learning methods

The steps of a typical QSAR research in the OCHEM system and the corresponding features are summarized in a diagram in the following figure:

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