awesome-drug-pair-scoring
PyNeuraLogic
| awesome-drug-pair-scoring | PyNeuraLogic | |
|---|---|---|
| 4 | 7 | |
| 85 | 267 | |
| - | - | |
| 0.0 | 8.0 | |
| over 1 year ago | 4 days ago | |
| Python | ||
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-drug-pair-scoring
- A Unified View of Relational Deep Learning for Drug Pair Scoring
- Show HN: A Unified Model of Deep Learning for Drug Pair Scoring
-
[R]
Git: https://github.com/AstraZeneca/polypharmacy-ddi-synergy-survey
- Show HN: Drug-drug interaction, synergy, and polypharmacy prediction
PyNeuraLogic
-
[P] PyNeuraLogic - a framework for writing differentiable logic programs
Hi, sure. With this framework, you can write and train deep learning models similarly to PyTorch or TensorFlow. Although the main aim of PyNeuraLogic is on deep relational learning and it uses custom declarative language (implemented in Python). Best fitting use cases are everything where you can utilize relations. One of those use-cases that we are promoting right now is on Graph Neural Networks (GNNs), where you have relations between nodes (such as social networks, molecules). You can then utilize those relations and do regular tasks on graphs, such as link prediction, graph classification, node classification, etc. GNNs quite nicely fit the framework and its language and can be expressed just in one line (as shown in the README). The concrete use-case of PyNeuraLogic on GNNs could then be a molecule classification (example). Other use-cases could be for NLP (we have todo to write an example for it) or knowledge base completion. You could also use it like a regular framework without utilizing relations, but in that case, it might be more efficient to go with PyTorch or TensorFlow.
- Show HN: Evaluate Deep Learning models directly in a database with PyNeuraLogic
-
Why Hypergraphs? (2013)
For an original proposal that do logic inference on Hypergraphs I am using NeuraLogic, through a Python frontend (https://github.com/LukasZahradnik/PyNeuraLogic)
I wonder if this is something the author would have enjoyed…
-
This Week in Python
PyNeuraLogic – PyNeuraLogic lets you use Python to create Differentiable Logic Programs
- GitHub - LukasZahradnik/PyNeuraLogic: PyNeuraLogic lets you use Python to create Differentiable Logic Programs
- Show HN: PyNeuraLogic: Python Differentiable Logic Programs
What are some alternatives?
efficient-gnns - Code and resources on scalable and efficient Graph Neural Networks
reloadium - Hot Reloading and Profiling for Python
diffnet - Graph Neural Network based Social Recommendation Model. SIGIR2019.
chemicalx - A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
PDN - The official PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing" (WebConf '21)
hatch - Modern, extensible Python project management
pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
pytorch_geometric - Graph Neural Network Library for PyTorch [Moved to: https://github.com/pyg-team/pytorch_geometric]
typedb-ml - TypeDB-ML is the Machine Learning integrations library for TypeDB
pygod - A Python Library for Graph Outlier Detection (Anomaly Detection)
GNNPapers - Must-read papers on graph neural networks (GNN)
gradio - Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!