Metadata-Version: 2.4
Name: MSIonization
Version: 1.0.0
Summary: an ML tool for ionization prediction of small molecules
Home-page: https://github.com/sepehribakhtyar/MSIonization
Author: Yatendra Singh, Bakhtyar Sepehri, Zeyad Ibrahim, Robert J. Doerksen, Sixue Chen
Author-email: ysingh@olemiss.edu,bsepehri@olemiss.edu, zmibrahi@go.olemiss.edu, rjd@olemiss.edu, schen8@olemiss.edu
License: CC BY-NC
Requires-Python: >=3.9.21
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: rdkit
Requires-Dist: joblib
Dynamic: author
Dynamic: author-email
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# MSIonization
MSIonization is a Python package for predicting the optimal ionization mode with higher ionization efficiency for organic molecules from their SMILES representations.

# Features
1- Predict the optimal ionization mode (positive or negative) from SMILES using a Support Vector Machine (SVM) model.

2- Performs applicability domain analysis.

3- Displays prediction probabilities for both classes.

4- GUI support for easy use. 

5- Multiprocessing support.

6- Error handling for invalid SMILES.

# Built With

-Python: Recommended version is python ≥3.9.21.

-Fingerprint calculation: [RDKit 2022.09.5](https://www.rdkit.org) 

-Machine Learning: [Scikit-learn 1.6.0](https://scikit-learn.org)

-Applicability Domain: Based on calculating leverage.


# Link for more information:

https://github.com/sepehribakhtyar/MSIonization

