Application of artificial intelligence in drug-target prediction

One of the most important problems in biomedicine is still drug development. AI-assisted drug development has emerged as a new trend, especially in anticipating drug-target relationships, as a result of the big data era and the quick growth of information technologies like artificial intelligence (AI). By efficiently extracting information from intricate biological data, properly modeling molecular interactions, and reliably forecasting possible drug-target outcomes, AI-driven models have become viable tools to tackle the problem of drug-target prediction. Convolutional neural networks (CNNs), graph convolutional networks (GCNs), transformers, and other sophisticated deep learning designs, as well as traditional machine learning (ML) and network-based approaches, are essential. Drug-target interaction prediction is a challenging task. AI-based DTI prediction can greatly increase speed, lower costs, and screen possible drug design choices prior to real tests thanks to the emergence of artificial intelligence (AI) techniques like machine learning and deep learning. Nevertheless, there are a number of issues with the use of AI techniques that must be resolved. This article examines several AI-based strategies and makes recommendations for potential future developments.