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Supervised learning techniques to predict compounds in pathway modules based on molecular properties

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posted on 14.05.2021, 04:54 by Hayat Ali ShahHayat Ali Shah
# Machine learning Classifiers for prediction of Pathway module & it classes
We use SMILES representation of query molecules to generate relevant fingerprints, which are then fed to the machine learning classifiers ETC for producing binary labels corresponding pathway module & its classes. The details of the works are described in our paper.
A dataset of 6597 downloaded from KEGG, 4612 compounds either belong or not to Pathway module in metabolic pathway the remaining 1985 compounds belong to module classes prediction problems
### Requirements
*Chemoinformatics tools
* Python
* scikit-learn
* RDKit
* Jupyter Notebook
### Usage
We provide two folder containing Classifiers files,grid search for optimization of hyperparameters, and datasets(module, module classes

Funding

National Key R&D Program of China (No.2019YFA0904303),

Major Projects of Technological Innovation in Hubei Province (2019AEA170)

Frontier Projects of Wuhan for Application Foundation (2019010701011381)

History

Email Address of Submitting Author

hayatali@whu.edu.cn

ORCID of Submitting Author

0000-0001-7043-8081

Submitting Author's Institution

Wuhan University China

Submitting Author's Country

Pakistan

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