Abstract
Abstract: Cancer is a condition in which body cells grow uncontrollably and spread to other parts of the body or grow at a particular location. Depending on their location, cancer is named or categorized. Breast cancer is the second most constantly determined and one of the prime reasons for cancer death among females. Many external factors like carcinogenic agents and internal factors like genetic factors are responsible for causing breast cancer in females. Additionally, the threat of breast cancer occurrences increases with age and non-success in treatment. The current methods and treatments utilized in treating, diagnosing and predicating breast cancer in the present world are not very advanced. Therefore, over time, the desire to analyze the factors facilitating the succession of breast cancer, prediction, and reduction in the time taken for diagnostics, treatment, and drug discovery for breast cancer has increased. However, traditional methods make it hard to study prediction, diagnostics, treatment, and drug discovery for breasts. Therefore, computational approaches like artificial intelligence, bioinformatics, quantitative structure-activity relationship (QSAR) studies, and molecular docking are used to analyze those things. This article discusses current trends in computational chemistry in different fields.
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