Introduction to Graph Machine Learning
More from the blog
Making machines that can listen
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
Learner collaborates with MIT peers to develop book of AI insights
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
The sleep framework: A new way for LLMs to retain knowledge in continual learning
Education Innovation
Q&A: How AI came to model the “messy” human mind
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
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
PepPCBench enables a robust evaluation of PFNN-based methods and supports their continued development for peptide-protein structure prediction, and highlights the influence of peptide length, conformational flexibility, and training set similarity on prediction accuracy.
Unified and explainable molecular representation learning for imperfectly annotated data from the hypergraph view
OmniMol is presented, a framework using hypergraphs to improve predictions of molecular properties, addressing challenges of imperfect data annotation and enhancing model explainability, and achieves state-of-the-art performance in properties prediction.
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.