bidd-molmap
graphein
bidd-molmap | graphein | |
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1 | 2 | |
134 | 988 | |
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4.2 | 7.8 | |
about 2 months ago | 17 days ago | |
Jupyter Notebook | Jupyter Notebook | |
GNU General Public License v3.0 or later | MIT License |
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bidd-molmap
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Researchers From Tsinghua University, The National University of Singapore Have Recently Developed An AI Tool Called ‘MolMapNet’ That Predicts Drugs Pharmaceutical Properties
Github: https://github.com/shenwanxiang/bidd-molmap
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?
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AllenSDK - code for reading and processing Allen Institute for Brain Science data
TensorFlow-Examples - TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
fastai - The fastai deep learning library
DrugDiscovery-Tox21 - Drug discovery project by making use of the Tox-21 dataset.
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