Prediction of Drug Target Interactions Based on Artificial Intelligence

Abstract

Drug-target interaction (DTI) prediction is an efficient pre-screening method that uses algorithmic models to assess the binding potential of drug molecules to protein targets. Current research is accelerating towards the integration of heterogeneous graph neural networks, protein language models, and generative artificial intelligence. This review systematically summarizes the latest developments in these technologies, pointing out the problems currently being addressed in research such as data sparsity and cold start, as well as the manifestations of general machine learning challenges such as recommendation systems and noise learning in the biomedical field; Interpret representative models such as Dual Heterogeneous Graph Transformer for Drug-Target Interaction (DHGT-DTI) and Graph Positional encoding and Sequence features for Drug-Target Interaction (GPS-DTI). The combination of dual perspective learning, equivariant graph convolution, and attention mechanism enhances the understanding and reasoning ability of these models in complex biological networks. The generative artificial intelligence diffusion model has opened up a path for developing new drugs through structured and data enhanced approaches. Research has shown that important issues related to computational performance, interpretability, and data compatibility still need to be addressed in existing models. Building a high-performance, multifunctional pre trained model for large-scale biomolecules should be a key direction for development.