Dive into the wealth of knowledge with these handpicked awesome lists, each a treasure trove of resources in the realms of Graph Neural Networks and Drug Discovery. Whether you’re seeking scholarly papers, datasets, tutorials, or software, these repositories serve as your gateway to the cutting-edge developments and foundational insights in these dynamic fields.
Awesome-Graph-Neural-Networks: Your directory to scholarly papers on GNNs.
Awesome Self-Supervised Learning for Graphs: Curated insights into self-supervised learning within graph domains.
Awesome Graph Self-Supervised Learning: A curated list for awesome self-supervised graph representation learning resources.
Awesome-self-supervised-gnn: Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).
Awesome-Graph-Embedding: A compendium of Graph Embedding techniques.
Awesome gnns on large-scale graphs: Focused discussions and studies on GNN application in large-scale graph environments.
Awesome Deep Graph Representation Learning: A curated list dedicated to the art and science of graph representation learning.
Awesome Imbalanced Learning on Graphs: A collection addressing the challenges of imbalanced learning on graphs.
Knowledge-graph-learning: Your ultimate guide to knowledge graph tutorials, projects, and communities.
Awesome Partial Graph Machine Learning: A curated list for awesome partial graph machine learning resources.
Awesome Deep Graph Clustering: Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).
Awesome-Deep-Graph-Anomaly-Detection: Awesome graph anomaly detection techniques built based on deep learning frameworks. Collections of commonly used datasets, papers as well as implementations are listed in this github repository.
Literature of Deep Learning for Graphs: This is a paper list about deep learning for graphs.
Awesome-drug-discovery-knowledge-graphs: A rich repository accompanying the paper on biomedical datasets from a knowledge graph perspective.
Awesome-HealthCare-KnowledgeBase: A curated list of awesome healthcare taxonomies and knowledge graphs.
Awesome-Knowledge-augmented-GML-for-Drug-Discovery: A collection of resources related with Knowledge-augmented Graph Machine Learning for Drug Discovery.
Awesome Deep Graph Learning for Drug Discovery: A curated list of papers on deep graph learning for drug discovery.
Awesome-drug-discovery: A collection of drug discovery, classification and representation learning papers with deep learning.
Awesome Cheminformatics: A curated list of Cheminformatics libraries and software.
Awesome-small-molecule-ml: A curated list of resources for machine learning for small-molecule drug discovery.
Awesome-pretrain-on-molecules: A curated list of resources for chemical pre-trained models.
Awesome-explainable-graph-reasoning: A collection of research papers and software related to explainability in graph machine learning.
Awesome-python-chemistry : A curated list of Python packages related to chemistry.
Deep Learning for Graphs in Chemistry and Biology: This is a paper list of deep learning on graphs in chemistry and biology from ML community, chemistry community and biology community.