Welcome to the Predictor webserver

- Choose model -

• Use the dropdown menus to choose the property you wish to model :
• Select a general kind of property - Here you can select the category of the property you wish to model.
• Select a property to model - Choosing a general kind of property will fill this dropdown menu with the list of available and modellable properties.
• "ColorAtom" option :
• Generate images of the query(ies) with ColorAtom - indicates the influence of each atom in queried molecule(s) using color code.

- Molecules input -

• There are 2 ways of inputting molecules :
• Draw a single molecule - You can draw a single molecule, or upload a .mol file of a single molecule in the sketcher.
• Upload a file - If you with to use the model on more than one molecule, then you should upload a .sdf file containing your molecules.

- Output -

• Description of output columns provided at the end of the prediction process :
• Predicted value - predicted class label or numeric value depending on the chosen model.
• Applied models - number of applied individual models / total number of individual models in the consensus model.
• Prediction confidence - prediction confidence label represented in a color code.
  • Green - Optimal
  • Blue - Good
  • Orange - Average
  • Red - Unreliable
• 2D structure - 2D structure of compounds after standardization. If "ColorAtom" option was toggled, atoms in the structure will be colored based on their influence on the property according to the model.
  • For classification models : a color reflects the contribution to the predicted class. The darker the color the higher the contribution.
  • For regression models : a color reflects its positive (blue) or negative (red) increment to the modeled property.
• Comments - detailed information about the prediction.
  • AD satisfaction shows the percentage of individual models for which applicability domain was satisfied.
  • For classification models, repartition of classes represents the agreement of predicted classes among the applied models.
  • For regression models, standard deviation over the predicted values of each applied model is given.