Abstract
Integrating Pharmacological Mechanisms into Graph Convolutional Networks for Enhanced Drug Response Prediction and Disease Classification
Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, 1China Petroleum Technology and Development Corporation, Beijing 100028, China
Correspondence Address:
Xiaotong Zhang, Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China, E-mail: zxt@ustb.edu.cn
This study proposes a novel approach to drug response prediction by incorporating pharmacological mechanisms into graph convolutional networks. The developed model, termed pharmacological knowledge graph convolutional network, leverages prior knowledge of drug-target interactions, pharmacokinetics, and pharmacodynamics to improve both the accuracy and interpretability of predictions. By integrating comprehensive pharmacological datasets such as DrugBank and the Cancer Genome Atlas, the pharmacological knowledge graph convolutional network model demonstrates enhanced predictive capabilities, outperforming baseline models that lack prior knowledge integration. Experimental results highlight the potential of this approach in advancing personalized medicine and drug repurposing strategies by offering deeper insights into drug mechanisms across diverse biological contexts. Pharmacological knowledge graph convolutional network’s ability to predict drug responses accurately underscores its utility in the development of tailored therapeutic interventions and the acceleration of new drug discoveries
PDF
