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

# 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