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

• A few newly introduced molecular descriptors were discussed.
• Various computational approaches to calculate the descriptors are listed.
• We described several methods for descriptors selection for building high predictive QSAR models.
• Advantage and disadvantages of selection methods were also addressed.
• Studies successfully applied the descriptors and their selection methods were also addressed.

The screening of chemical libraries with traditional methods, such as high-throughput screening (HTS), is expensive and time consuming. Quantitative structure–activity relation (QSAR) modeling is an alternative method that can assist in the selection of lead molecules by using the information from reference active and inactive compounds. This approach requires good molecular descriptors that are representative of the molecular features responsible for the relevant molecular activity. The usefulness of these descriptors in QSAR studies has been extensively demonstrated, and they have also been used as a measure of structural similarity or diversity. In this review, we provide a brief explanation of descriptors and the selection approaches most commonly used in QSAR experiments. In addition, some studies have also demonstrated the positive influence of features selection for any drug development model.