Build your Unique 2D-QSAR model
Get your Data, Statistical Evaluation and Predict activity value of your own molecule

Regression Results (Your regression model/equation, where y = Predicted Activity value, & x = LogP value)

Statistical evaluation parameters to proof the robustness of unique 2D-QSAR model

Predict Activity value of your own designed molecule by entering LogP value

Enter LogP: Predict Activity

QSAR Modeling Process Overview

The Quantitative Structure-Activity Relationship (QSAR) modeling process is a powerful approach used to establish predictive relationships between chemical structures and their biological activities. In the context of this activity scaffold model, the QSAR model relies on two primary input variables: the **LogP** (a descriptor of lipophilicity) and **Activity** (the biological or chemical response of interest). The key steps involved in building this QSAR model are as follows:

1. Data Preparation

The initial step in QSAR modeling involves collecting and preparing data. In this example, two activity scaffolds are provided, each containing a series of compounds with their respective LogP values and associated activity values. The dataset is structured in a table format where each row represents a compound, with columns for the compound ID, LogP, and Activity.

2. Regression Analysis

The heart of QSAR modeling is the development of a mathematical model that relates the LogP values (predictor variable) to the Activity values (response variable). This is done through **linear regression** analysis, where the relationship between LogP and Activity is expressed as a straight line equation:

y = mx + b

In this equation, **y** is the predicted Activity, **x** is the LogP value, **m** is the slope (which indicates the strength and direction of the relationship), and **b** is the y-intercept. The model is built using statistical methods to calculate the optimal values for **m** and **b** that best fit the data.

3. Model Evaluation

After constructing the regression model, it is essential to assess its quality and reliability. The two primary evaluation metrics used here are **R²** (coefficient of determination) and **MSE** (Mean Squared Error):

• R²: This value indicates how well the model fits the data, with a value of 1 meaning a perfect fit. A higher R² means that a greater proportion of the variability in Activity is explained by the LogP values.
• MSE: This metric quantifies the average squared difference between the observed actual outcomes and those predicted by the model. Lower MSE values indicate better prediction accuracy.

Additionally, cross-validation methods like **5-Fold Cross-Validation** and **Leave-One-Out Cross-Validation (LOO-CV)** are performed to validate the model's robustness and its ability to generalize to new, unseen data.

4. Randomization and Robustness Testing

To further test the model's validity, **Y-Randomization** is used, where the Activity values (Y) are shuffled randomly to assess whether the relationship between LogP and Activity is genuine or just coincidental. A lower **Q²** value from Y-randomization suggests that the model is robust and the relationship is real.

5. Predictive Application

Once the model is validated, it can be applied to make predictions. For example, by entering a new LogP value into the model, the system can predict the corresponding Activity for that compound. This is useful for virtual screening, where new compounds can be evaluated without experimental testing.

Conclusion

In summary, QSAR modeling is a powerful computational technique that allows for the prediction of biological activity based on molecular properties. By leveraging regression analysis, model validation, and cross-validation, this approach can be used to predict the effectiveness of new compounds, potentially speeding up the drug discovery process or aiding in the design of safer chemicals.

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