A glimpse at molecular descriptor selection algorithms in QSAR studies: including advantages and problems
TL;DR
The main purpose of this paper is to review feature selection methods in QSAR studies, which there are two main categories, supervised and unsupervised.
Abstract
Abstract One of the most frequently used methods in computational drug design is quantitative structure-activity relationship (QSAR). The main purpose of QSAR modeling is to estimate the relationship between chemical structures and biological activity in a group of molecules. In this method, molecules that have the greatest impact and the least side effects can be identified and extracted among huge numbers of molecular compounds. The molecular descriptors play a crucial role in QSAR design, and contain the physical, chemical, and geometric information. This information is called a feature that acts as an input to the QSAR model. Today, with the design of various applications to calculate molecular descriptors, the information obtained for each chemical structure is rising day by day which could lead to serious problems such as redundancy and over fitting. To solve this problem, researchers have used various techniques such as feature selection to improve the results of the model. The important point is that if the features are not properly selected, the QSAR model will fail. Up to now, different algorithms have been proposed to select the descriptors, which there are two main categories, supervised and unsupervised. The main purpose of this paper is to review feature selection methods in QSAR studies.