Overview of machine learning approaches for drug-target interaction prediction

This comparision highlgihts the key aspects of several research studies focused on predicting drug interactions and drug-target associations using various machine learning techniques. The studies use various datasets such as DrugBank, LINCS signatures, and biological databases, employing algorithms like convolutional neural networks, graph convolutional networks, and deep learning methods. Evaluation metrics include accuracy, F-score, area under the curve (AUC), and precision-recall metrics, showing advancements in computational methods for pharmacological research.