Predicting drug-target interactions using graph neural networks: A deep learning approach
Narla Swamy Pavan Koushik, Peddinti Priyanka, S R Reeja · 2025
The procedure of drug-target interaction prediction (DTI) stands as a vital process during drug discovery because it helps reveal which drug substances interact with which biological targets including proteins and receptors. For example, the interactions themselves are too complex to be predictively modelled using single type biological or chemical data; they likewise are so large and varied that approaches traditionally used to predict these interactions are not powerful enough. We use this paper to look at how graph neural networks (GNNs) are used to model molecular structures and predict drug target. GNNs guard their strength in modelling molecular structures because they master the process of detecting atomic and bond relationships thus producing precise molecular representations. The authors evaluate convolutional neural networks (CNNs) and recurrent neural networks (RNNs) alongside GNNs for their use in DTI prediction. The system accepts molecular graphs as its starting data to calculate the predicted interaction probability between drugs and their target proteins. Progressive experimentation has proven the model effectiveness which enables it to reduce drug candidates for experimental verification. The drug discovery process can reach improved efficiency in DTI prediction and accelerate therapeutic development according to results from machine learning (ML) particular GNNs.