Abstract
Predicting biomolecular interactions is a crucial task in drug discovery and molecular biology. Deep learning, with its ability to learn complex patterns from large datasets, has shown promising results in predicting biomolecular interactions. In this review, a comprehensive and accessible overview of deep learning algorithms is aimed to provide that can enhance the prediction of biomolecular interactions using various features, including sequence data, structural information, and functional annotations. The datasets and models for predicting biomolecular interactions using deep learning are summarized. These deep learning models are developed for a wide range of target molecules, including proteins, nucleic acids, and small molecules, thus reducing the time and cost of screening compounds with high binding affinity to a given target. Furthermore, deep learning can also aid in understanding the mechanisms of biomolecular interactions by identifying key residues involved in the interaction, and help in predicting the side effects of drugs by identifying potential off-target interactions. In conclusion, deep learning has the potential to revolutionize drug discovery and improve understanding of molecular biology by providing accurate and efficient prediction in biomolecular interactions.