Introduction to Graph Machine Learning
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Related papers
Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction
MCGLPPI, a novel geometric representation learning framework that combines graph neural networks (GNNs) with the MARTINI molecular coarse-grained (CG) model to predict overall PPI properties accurately and efficiently, offers an effective and efficient solution for PPI overall property predictions.
PepPCBench is a Comprehensive Benchmarking Framework for Protein-Peptide Complex Structure Prediction
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Unified and explainable molecular representation learning for imperfectly annotated data from the hypergraph view
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DRHIN: An Integrated and Interactive Web Server for Drug Repositioning
The DRHIN platform provides a code-free portal supporting three key predictive tasks: discovering drug-disease associations, repurposing existing drugs for new indications, and identifying potential therapies for specific diseases, making analyses accessible and reproducible.