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In Silico Drug Designing of Breast Cancer in Homo sapiens

Srishti Singh


Breast cancer is a severe health concern in India, with the highest mortality rate among women. It is brought on by uncontrolled cell division and has the potential to spread to other body regions. As a transcription factor, interactions with estrogen receptor (ER) alpha are primarily responsible for malignant tumors through regulating the transcription of numerous genes. Because most of the medications now used to treat breast cancer have significant side effects, we concentrated on natural chemicals that have no detrimental impact on normal human cells. The specific type of breast cancer can affect either one breast or both breasts. In the body, cancer develops when cells multiply uncontrollably and infiltrate surrounding tissues. Breast cancer can strike either a man or a woman. Most breast lumps are benign, meaning they do not spread or result in cancer. The risk of developing cancerous cells may occasionally increase as a result of these benign breast lumps. This project has been done to find the most suitable drug for breast cancer. Here, we have used in silico technique to find the most suitable drug. First of all, we have selected the target protein responsible for breast cancer with the help of the KEGG pathway database and then we found its isoforms using PDB (protein data bank). Secondly, we have selected three market drugs responsible for curing breast cancer and then we have docking of the market drugs and ligands with the protein isoforms by using Molegro Virtual Docker. After docking, we can evaluate the energy and find the best drug from our results. The ligand with the highest minimum energy will be considered the best one to use in the future


Breast cancer, databases, drug discovery, KEGG pathway, molecular docking

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