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Knowledge Graphs and Graph Neural Networks for Early Drug Target Characterization
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Andrei Zinovyev, In Silico R&D Department, Evotec
Biomedical knowledge graphs serve as powerful data integration platforms and are widely utilized in early drug discovery by biotech and pharmaceutical companies. Knowledge graphs facilitate efficient retrieval of existing data and augment this with predictive inference, leveraging advanced analytical tools such as graph neural networks (GNNs). In this presentation, we will demonstrate the application of predictive approaches to two representative tasks in drug target characterization. The first application involves the analysis of high-throughput cell imaging, where GNN-based modeling aids in evaluating the novelty of experimentally determined genetic associations. The second application showcases the use of GNNs to develop predictive AI models for early drug target safety assessment (TSA), integrated into Evotec's internally developed TSA platform.
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This explores how the integration of knowledge graphs and advanced machine learning techniques, such as graph neural networks, can accelerate the identification and validation of drug targets, improving early-stage drug discovery and therapeutic development. water heater repair denver colorado
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This blog explores how knowledge graphs and graph neural networks (GNNs) are revolutionizing early drug target characterization in pharmaceutical research. By integrating data from various sources, knowledge graphs enhance data retrieval and prediction accuracy, particularly in high-throughput cell imaging and drug target safety assessment (TSA). The use of GNNs helps identify novel genetic associations and predict safety outcomes for drug development. This innovative approach exemplifies the power of AI in early-stage drug discovery. On a different note, imagine a stylish jacket red and white, complementing this cutting-edge research.