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[ Customized Score | Binary Classification ] Multi Parameter Optimzation (MPO) is a method that can be used to derive a score for the relative importance of a number of different chemical properties. You can create your own scores or use the ones built into ICM: For example:
About the MPO Method
About the MPO desirability functions in the MPO table The MPO is grouped into a table where each row represents a single property:
To run MPO:
[ Special Cases | Step Function | Save and Apply ] MPO step function shape is define by 3 parameters: Low, High, SlopeRun (resize column to see the full name) Below picture illustrates meaning of these parameters.
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Custom step function can be defined using logical expression in the 'CustomStepFunc' column:
![]() Note that MPO expects each result from step function to be in range [0-1], so the expression should be normalized (e.g: as shown below)
(x<=25?2.2:x<=45?1.8:x<=65?1.4:1.2)/2.2
Another step function example molWeight_mol<250 ? 0 : molWeight_mol<=300 ? 0.2 : molWeight_mol <= 450 ? 1. : molWeight_mol <= 500 ? 0.2 : 0.
Saving and Applying the MPO ModelThe MPO model can be saved in .tab or .icb format. To save the model, right-click on the myMPO table header tab and choose the appropriate option. To reuse an MPO model:
Important Considerations
Binary classification using Multi-Parameter Optimization (MPO) and Random Forest is a method for categorizing compounds into one of two groups (e.g., active/inactive, toxic/non-toxic, or drug-like/non-drug-like) based on multiple molecular properties. Instead of ranking compounds on a continuous scale, this approach applies a pass/fail decision based on predefined criteria. The classification process involves:
Download an example file here. In this example file the column 'cls' contains the binary column with cl' binary column with 1/0 active/non-active
Creating and Optimizing an MPO for Binary Classification
Multi-Parameter Optimization (MPO) for binary classification enables the selection and optimization of molecular properties to distinguish between two classes, such as active (1) / non-active (0). This approach uses statistical thresholds and machine learning to refine property-based classification models.
Workflow for MPO Binary ClassificationOpen the menu - go to Chemistry/MPO/Create/Optimize MPO for Binary Classification1. Select the Classification ColumnChoose the binary column (e.g., 'cls' with 1 for active and 0 for non-active).
2. Select Numerical Features for MPOIn the 'Columns For MPO' section select relevant molecular descriptors to be included in the optimization process. In this example you could choose the properties shown below. ![]()
3. Feature Selection via Random Forest (RF) ClassificationThe top <Top_Percent> most important features are selected based on an RF model.
4. Initial MPO GenerationFor each selected feature:
5. Optimization of MPO ParametersThe Low, High, and Slope values are refined using the Amoeba minimizer to maximize the Area Under the Curve (AUC) of the classification model. ![]()
Final AUC Score: Once optimization is complete, the start and final AUC values are reported in the terminal window. The result MPO will then contain optimized parameters. ![]()
This automated approach ensures an optimized MPO model that effectively classifies compounds while maximizing predictive performance. You can save the model and apply it to another chemical table as described here.
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