One of the most commonly used in silico approaches for assessing new molecules' activity/property/toxicity is the Quantitative Structure-Activity/Property/Toxicity Relationship ( QSAR/QSPR/QSTR), which generates predictive models for efficiently predicting query compounds . QSAR/QSPR/QSTR uses numerical chemical information in the form of molecular descriptors and correlates these to the response activity/property/toxicity using statistical techniques. While QSAR is essentially a similarity-based approach, the occurrence of activity/property cliffs may greatly reduce the predictive accuracy of the developed models. The novel Arithmetic Residuals in K-groups Analysis (ARKA) approach is a supervised

dimensionality reduction Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally ...

technique developed by th
DTC Laboratory
Jadavpur University that can easily identify activity cliffs in a data set. Activity cliffs are similar in their structures but differ considerably in their activity. The basic idea o
the ARKA descriptors
is to group the conventional QSAR descriptors based on a predefined criterion and then assign weightage to each descriptor in each group. ARKA descriptors have also been used to develop

classification Classification is the activity of assigning objects to some pre-existing classes or categories. This is distinct from the task of establishing the classes themselves (for example through cluster analysis). Examples include diagnostic tests, identif ...

-based and regression-based QSAR models with acceptable quality statistics.
The ARKA descriptors
have been used for the identification of activity cliffs in QSAR studies and/or model development by multiple researchers.
tutorial presentation
on the ARKA descriptors is available. Recently a multi-class ARKA framework has been proposed for improved

q-RASAR The quantitative Read-Across Structure-Activity Relationship (q-RASAR) concept has been developed by thDTC Laboratory, Jadavpur Universityby merging Read-Across and QSAR. It is a statistical modeling approach that uses the similarity and error-b ...

model generation.

References

Cheminformatics Dimension reduction Long stubs with short prose {{Chem-stub