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- 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 (external) selection, decorrelation de-correlation filter, principal component analysis (PCA) based selectionUnsupervised Forward Selection (UFS)
- 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
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