1. Department of chemical engineering, Birjand University of technology, Birjand, Iran
Breast cancer is the most commonly diagnosed cancer among women and the leading cause of cancer death (14% of all cancer-related deaths) around the world. recent in vivo and in vitro studies have been demonstrated that several natural products including essential oils have significant breast anticancer activity. however several studies have been done to understanding the breast anticancer properties of essential oils, the anticancer activity of the various phytochemicals found in them is unknown. further laboratory research and clinical trials are needed to elucidate the effects of various essential oil constituents on breast cancer. due to large budget and time needed for these investigations, through virtual screening of essential oil compounds, discovery of new anticancer agents can be done in faster and more efficient way.
The present work has been developed a new qsar model for the efficient search of new anti-breast cancer agents in natural product against mcf7 cell line. the data set for developing the model consists of a diverse set of compounds and their corresponding descriptors, including 149 active (ic50 <= 0.001 µm) anticancer compounds and 96 inactive compounds. this data set is randomly divided into two splits, a training set of 176 compounds and a test set of 69 compounds. the training sets are used to build the qsar model, whereas the test set is used to evaluate the prediction ability of the model. the developed qsar model can be used for discovery of new anti-breast cancer agents in essential oils.
The gfa method was used to select the most important molecular descriptors (five descriptors) based on the training set and develop the optimal linear qsar model. the developed model exhibited accuracy higher than 88% in both training and prediction sets, and it can be used as tools for virtual screening of anti-breast cancer compounds. the virtual screening of essential oil compounds located within the applicability domain of model can be indicated potent anti-breast cancer agent (ic50 <= 0.001 µm) against mcf7 cell line.
In this work a quantitative structure–activity relationship (qsar) model was developed for detecting the new anti-breast cancer compounds against mcf7 cell line. the high accuracy of developed model for classification of train and test set, indicates it can be used for discovery of new anti-breast cancer compounds. the results of this work can be improved speed, simplicity and budget consuming in discovery and development of anticancer.
Breast cancer, qsar, essential oil, virtual screening;