Drug repurposing processing plays a significant role in evaluating Drug Target identification in the field of Drug discovery. Today Machine learning algorithms based automated systems are gaining attention for creating new device applications in the field of artificial intelligence. [2] The present study used machine learning approaches to develop a computational predicted model for predicting the unknown pharmacological effects with immune-oncology compound. The authors applied similarity features on drug and disease to see indication also authors observed that if number of descriptor is increased then accuracy of model is also increased. Classification linear and nonlinear algorithms such as logistic regression,Support Vector Machine (Kernel) and Random Forest were used to build the computational models. We used 10 fold Cross-validation on dataset for train and test data to performed best (Accuracy = 0.932, AUC-ROC = 0.971), finally, the developed model was used to anticipate probable indications for currently available medications for cancer protein.

Proteins are fundamental compounds in biological processes during the analysis of drug target indication for drug repurposing. The identification of relevant features is a necessary step in determining protein structure. A classification technique is used to identify the most important features in a dataset, which is why feature selection is so important. For protein structure prediction, recent research has developed a wide range of new methods to improve accuracy. The authors use principal component analysis (PCA) with correlation-matrix-based feature selection to analyse breast cancer data. In this paper, they discussed a therapeutic agent that is used to reduce the dataset by reduction-based algorithm and after that applied reduced dataset labelled as Standard Gold Dataset on machine learning model to analyze drug target indication. They get the higher accuracy of 92.8%, 93.9%, and 95.3%, each of the three datasets with 200, 500, and 1000 features with SVM with RBF kernel function. Also they found the best result, 97.8%, with the same classifier.