Deriving general structure-activity/selectivity relationship patterns for different subfamilies of cyclin-dependent kinase inhibitors using machine learning methods.

Cyclin-dependent kinases (CDKs) play essential roles in regulating the cell cycle and are among the most critical targets for cancer therapy and drug discovery. The primary objective of this research is to derive general structure-activity relationship (SAR) patterns for modeling the selectivity and activity levels of CDK inhibitors using machine learning methods. To accomplish this, 8592 small molecules with different binding affinities to CDK1, CDK2, CDK4, CDK5, and CDK9 were collected from Binding DB, and a diverse set of descriptors was calculated for each molecule.

Identification of molecular features necessary for selective inhibition of B cell lymphoma proteins using machine learning techniques.

Selective inhibition of Bcl-2 and Bcl-x proteins due to their dual inhibition toxicity plays an important role in treatment of cancer and chemotherapy effectiveness; therefore, in the last decade, discovery of selective inhibitors for Bcl-2 and Bcl-x proteins has become a significant and important research topic. The present contribution paves the way for characterization of molecular features which induce selectivity for inhibition of Bcl-2 and Bcl-x. In this line, a total of 1534 molecules related to inhibition of Bcl-2 and Bcl-x proteins were collected from Binding Database.