ICM GUI Manual
PrevICM User's Guide
10.34 Multi Parameter Optimization
Next

[ 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:

10.34.1 Customized MPO Score


[ 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.

10.34.1.1 Special Cases


10.34.1.2 Custom Step Function


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.

10.34.1.3 Save and Apply MPO Model


Saving and Applying the MPO Model

The 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

10.34.2 Create/Optimize MPO for Binary Classification


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:

  1. Defining Key Properties - Selecting relevant molecular descriptors such as molecular weight, logP, hydrogen bond donors/acceptors, and polar surface area.
  2. Setting Thresholds - Establishing acceptable property ranges for classification.
  3. Scoring and Decision Making - Applying a scoring function that integrates the selected properties and assigns a binary outcome (e.g., "pass" if the compound meets all criteria, "fail" otherwise).

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 Classification

Open the menu - go to Chemistry/MPO/Create/Optimize MPO for Binary Classification

1. Select the Classification Column

Choose the binary column (e.g., 'cls' with 1 for active and 0 for non-active).

2. Select Numerical Features for MPO

In 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) Classification

The top <Top_Percent> most important features are selected based on an RF model.

4. Initial MPO Generation

For each selected feature:

5. Optimization of MPO Parameters

The 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.


Prev
Select Duplicates
Home
Up
Next
Combinatorial Chemistry