Statistical methods used in qsar

  • What is regression analysis in QSAR?

    Like other regression models, QSAR regression models relate a set of "predictor" variables (X) to the potency of the response variable (Y), while classification QSAR models relate the predictor variables to a categorical value of the response variable..

  • Like other regression models, QSAR regression models relate a set of "predictor" variables (X) to the potency of the response variable (Y), while classification QSAR models relate the predictor variables to a categorical value of the response variable.
QSARs attempt to relate physical and chemical properties of molecules to their biological activities by simply using easily calculable descriptors and simple statistical methods like Multiple Linear Regression (MLR) to build a model which both describes the activity of the data set and can predict activities for
The simple linear regression method performs a standard linear regression calculation to generate a set of QSAR equations that includes one equation for each independent variable. Each equation contains one variable from the descriptor set.

1 Importance of Metrics For Determination of Quality of QSAR Models

Advancement in fast and economical computational resources makes it feasible to compute a large number of descriptors using various software tools.
As a consequence, one cannot deny the risk of chance correlations with the increasing number of variables included in the QSAR model as compared to the limited number of compounds usually employed for t.

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4 Validation Metrics Employed in Classification-Based QSAR

Validation metrics can assess the performance of the classification-based models in terms of accurate qualitative prediction of the dependent variable.
Commonly applied metrics for classification-based QSAR models are illustrated below [20]:

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How does QSAR work?

QSAR attempts to correlate structural, chemical, statistical, and physical properties with biological potency using various mathematical methods.
The generated QSAR models are used to predict and classify the biological activities of new chemical compounds.

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How machine learning is used in QSAR/QSPR modeling?

In addition to the conventional methods, various machine learning tools are also useful for QSAR/QSPR modeling analysis especially for studies involving high-dimensional and complex chemical information data bearing a nonlinear relationship with the response under consideration.


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