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Drug Discovery and Development process refers to the process through which a new chemical compound is discovered, produced, and brought to market to treat a specific disease or medical condition. Today's datasets are often big in size and contain hundreds or thousands of characteristics so we need to extract meaningful information from them via automated content analysis. Machine Learning (ML) approaches encompass a diverse set of statistical algorithms for evaluating data, identifying shared patterns, deriving user models, and generating predictions. In this paper we have established biological interaction by analysing the drug molecule structure and protein structure for getting the relationship of Drug and Target for breast cancer that after we performed the optimization on prepared biological dataset that is Standard Gold Dataset (SGD). For optimization, we built machine learning model using linear machine learning algorithm such that Logistic Regression, Linear Discriminant Analysis (LDA) and Support Vector Machine and classify our Standard Gold Dataset (SGD). Logistic Regression is performing better above-mentioned linear machine Learning algorithms.