DeepPurpose
graphein
| DeepPurpose | graphein | |
|---|---|---|
| 1 | 2 | |
| 919 | 986 | |
| - | - | |
| 4.6 | 7.8 | |
| 7 months ago | 10 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| BSD 3-clause "New" or "Revised" License | MIT License |
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DeepPurpose
graphein
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Meet Graphein: a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures and Interaction Networks
Github: https://github.com/a-r-j/graphein
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[Discussion] which NN architecture is best suitable for analysing the structural data of biomolecules
As alluded to by u/WorldWar1Nerd, it depends on the format and structure of your data. However, based on what you have already said about your dataset, a graph neural network (GNN) may be a suitable choice, depending on the task. I recommend looking into a wonderful ML library for proteins called Graphein (https://github.com/a-r-j/graphein) to get started, however, do not be afraid if you find that you need to customize these methods to your specific problem.
What are some alternatives?
awesome-drug-pair-scoring - Readings for "A Unified View of Relational Deep Learning for Drug Pair Scoring." (IJCAI 2022)
pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
equidock_public - EquiDock: geometric deep learning for fast rigid 3D protein-protein docking
AllenSDK - code for reading and processing Allen Institute for Brain Science data
fastai - The fastai deep learning library
netsci-labs - (In progress) Network science laboratories. Covers graph theory, random graphs and ML on graphs
ktrain - ktrain is a Python library that makes deep learning and AI more accessible and easier to apply
geometric-gnn-dojo - Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks.
benchmarking-gnns - Repository for benchmarking graph neural networks