Current approaches for choosing feature selection and learning algorithms in quantitative structure–activity relationships (QSAR)

ABSTRACT

Introduction: Quantitative structure-activity/property relationships (QSAR/QSPR) are statistical models which quantitatively correlate quantitative chemical structure information (described as molecular descriptors) to the response end points (biological activity, property, toxicity, etc.). Important strategies for QSAR model development and validation include dataset curation, variable selection, and dataset division, selection of modeling algorithms and appropriate measures of model validation.

Areas covered: Different feature selection methods and various linear and nonlinear learning algorithms are employed to address the complexity of data sets for selection of appropriate features important for the responses being modeled, to reduce overfitting of the models, and to derive interpretable models. This review provides an overview of various feature selection methods as well as different statistical learning algorithms for QSAR modeling at an elementary level for nonexpert readers.

Expert opinion: Novel sets of descriptors are being continuously introduced to this field; therefore, to handle this issue, there is a need to improve new tools for feature selection, which can lead to development of statistically meaningful models, usable by nonexperts in the fields. While handling data sets of limited size, special techniques like double cross-validation and consensus modeling might be more meaningful in order to remove the possibility of bias in descriptor selection.

KEYWORDS:

• QSAR
• feature selection
• learning algorithms
• validation

Article Highlights

• Important strategies for QSAR model development are dataset curation, variable selection, and dataset division, selection of modeling algorithms and appropriate measures of model validation.

• Different feature selection methods and various linear and nonlinear learning algorithms address the complexity of datasets.

• Novel sets of descriptors are being continuously introduced to this field.

• There is a need to improve new tools for feature selection, which can lead to development of statistically meaningful models

• Special techniques like double cross-validation and consensus modeling might be more meaningful in order to remove the possibility of bias in descriptor selection.

This box summarizes key points contained in the article.

Declaration of interest

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed

Reviewer Disclosures

One referee is an employee of NovaMechanics Ltd.

Additional information

Funding

The authors are supported by the Department of Pharmaceuticals of the Ministry of Chemicals & Fertilizers, the Government of India through the National Institute of Pharmaceutical Education and Research-Kolkata (NIPER-Kolkata) with a fellowship. They are also supported by the University Grants Commission, New Delhi for financial assistance under the UPEII scheme.