Identifying Candidates for Breast Cancer using Interactions of Chemicals and Proteins

Aim and Objective: Breast cancer is one of the major causes of cancer deaths in women worldwide. Therefore, it is necessary to discover novel drugs or design effective treatments for this disease. However, the research and development of drugs by using only experimental methods is always time-consuming and expensive. With the development of computer science, some advanced computational methods can make full use of known knowledge to design candidate drugs, thereby reducing the cost and time of experimental testing. Materials and Methods: A computational method was proposed to identify novel candidates for breast cancer. The approved drugs and genes of breast cancer were taken as the input of the method. The chemical-chemical interactions and chemical-protein interactions were adopted to extract possible candidates from large numbers of existing chemicals. The method included three stages, termed searching stage, filtering stage and selecting stage. In the searching stage, chemicals that have associations with approved drugs were extracted. Then, these chemicals were screened in the filtering stage to discard those that have no relationships with breast cancer related genes. Finally, a clustering algorithm, termed as EM clustering algorithm, was employed to identify the potential candidates in the selecting stage. Results: An extensive analysis of twenty-one chemicals related to the same category with approve drugs indicated that multiple selected candidates were confirmed to have anti-breast cancer activities by retrieving literature. Conclusion: This method can provide some valuable instructions for drug repositioning.