• Kandukuri Sree Sai Sampat Gupta
• Cherukuri Sai Manvija
• Annepu Rohit
• Telagani Siva Lakshman Ajith
• Dr. D Rama Krishna

DTI-GCNN (Drug-Target Interaction Prediction with Graph Convolution Neural Networks) presents an innovative framework for predicting drug-target interactions, which is crucial in proteomics and pharmaceutical research. Traditional methods often overlook the intricate graph structure of DTI networks, but DTI-GCNN efficiently utilizes this complexity. By treating DTI networks as graphs, the model captures both local and global interactions through graph convolutional layers, effectively integrating structural and semantic information. This approach showcases the potential of GCNNs to enhance DTI prediction accuracy, offering valuable insights for drug discovery and precision medicine.

Graph Convolutional Neural Networks (GCNNs), Deep learning, Webscraping, Drug target interaction

"DRUG TARGET INTERACTION PREDICTION USING GRAPH CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.c238-c247, April-2024, Available :http://www.jetir.org/papers/JETIR2404232.pdf

"DRUG TARGET INTERACTION PREDICTION USING GRAPH CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppc238-c247, April-2024, Available at : http://www.jetir.org/papers/JETIR2404232.pdf