Feature Selection Methods in QSAR Studies

Abstract

A quantitative structure-activity relationship (QSAR) relates quantitative chemical structure attributes (molecular descriptors) to a biological activity. QSAR studies have now become attractive in drug discovery and development because their application can save substantial time and human resources. Several parameters are important in the prediction ability of a QSAR model. On the one hand, different statistical methods may be applied to check the linear or nonlinear behavior of a data set. On the other hand, feature selection techniques are applied to decrease the model complexity, to decrease the overfitting/overtraining risk, and to select the most important descriptors from the often more than 1000 calculated. The selected descriptors are then linked to a biological activity of the corresponding compound by means of a mathematical model. Different modeling techniques can be applied, some of which explicitly require a feature selection. A QSAR model can be useful in the design of new compounds with improved potency in the class under study. Only molecules with a predicted interesting activity will be synthesized. In the feature selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus attention, while ignoring the rest. Up to now, many feature selection techniques, such as genetic algorithms, forward selection, backward elimination, stepwise regression, and simulated annealing have been used extensively. Swarm intelligence optimizations, such as ant colony optimization and partial swarm optimization, which are feature selection techniques usually simulated based on animal and insect life behavior to find the shortest path between a food source and their nests, recently are also involved in QSAR studies. This review paper provides an overview of different feature selection techniques applied in QSAR modeling.

Multivariate calibration of NIR spectroscopic sensors for continuous glucose monitoring

“…By removing those irrelevant features, the prediction performance and robustness of the calibration models can be improved, so reducing the dimension of a multivariate data set is one of the most important steps in data handling. Variable-selection and reduction techniques are used to simplify the interpretation of models with few variables, to improve the prediction performance, and to decrease the risk of overfitting and overtraining [122].…”

Predicting the DPP-IV Inhibitory ActivitypIC50Based on Their Physicochemical Properties

“…The selection of descriptors plays an important role in construction for the actual model. In this work, mRMR-BFS method (minimum redundancy maximum relevance-backward feature selection) [42, 43] was used for the selection of molecular descriptors. The support vector regression (SVR) model was established based on the feature selection results.…”

Descriptors and their selection methods in QSAR analysis: paradigm for drug design

“…Descriptor selection is an important step for several reasons [10], including: (i) using only a few descriptors increases the interpretability and understanding of resulting models; (ii) It can reduce the risk of overfitting from noisy redundant molecular descriptors; (iii) it can provide faster and cost-effective models; and (4) it removes the Q5 activity cliff. However, noisy, redundant, or irrelevant descriptors should be removed in a way that the dimension of the input space is reduced without any loss of significant information [3].…”