Principal component analysis of quantum mechanical descriptors data to reveal the pharmacological activities of oxindole derivatives

Abstract

Oxindole derivatives have been known to possess anticancer, antimicrobial, antiviral, antioxidative, and other pharmacological activities. A substantial group of oxindole compounds are studied as anticancer and antimicrobial agents. In order to reveal the structure property relationships of oxindole compounds for their biological activities, a number of oxindole molecules with known anticancer or antimicrobial properties were calculated with density functional theory and quantum mechanical descriptors have been generated for each molecule. Principal component analysis (PCA) was performed on the generated data to reduce the dimensions of descriptor space. By using various training sets along with validation sets in PCA routine, it has been shown that anticancer or antimicrobial properties of unknown oxindole molecules can be predicted with ∼ 85% accuracy. This study shows that apart from structural/spatial descriptors commonly used in quantitative structure activity relationship studies, quantum mechanical descriptors can be equally useful in identification of biological activities of drug candidates in pharmacological research.