ChemML

ChemML is a machine learning and informatics program suite for the analysis, mining, and modeling of chemical and materials data. Please check the ChemML website for more information.

ChemML documentation: https://hachmannlab.github.io/chemml

Code Design:

ChemML is developed in the Python 3 programming language and makes use of a host of data analysis and ML libraries(accessible through the Anaconda distribution), as well as domain-specific libraries. The development follows a strictly modular and object-oriented design to make the overall code as flexible and versatile as possible.

The format of library is similar to the well known libraries like Scikit-learn. ChemML will be soon available via graphical user interface provided by ChemEco. ChemEco is a general-purpose framework for data mining without coding. It also interfaces with many of the libraries that supply methods for the representation, preprocessing, analysis, mining, and modeling of large-scale chemical data sets.

Latest Version:

to find out about the latest version and release history, click here

Installation and Dependencies:

You can download ChemML from PyPI via pip.

pip install chemml --user -U

Here is a list of external libraries that will be installed with chemml:

numpy
pandas
tensorflow
scikit-learn
matplotlib
seaborn
lxml

Since conda installation is not available for ChemML yet, we recommend installing rdkit and openbabel (please install openbabel 2.x not openbabel 3.x) in a conda virtual environment prior to installing ChemML. For doing so, you need to follow the conda installer:

conda create --name my_chemml_env python=3.6
source activate my_chemml_env
conda install -c openbabel openbabel
conda install -c conda-forge rdkit
pip install chemml

Citation:

Please cite the use of ChemML as:

Main citation:

@article{chemml2019,
author = {Haghighatlari, Mojtaba and Vishwakarma, Gaurav and Altarawy, Doaa and Subramanian, Ramachandran and Kota, Bhargava Urala and Sonpal, Aditya and Setlur, Srirangaraj and Hachmann, Johannes},
journal = {ChemRxiv},
pages = {8323271},
title = {ChemML: A Machine Learning and Informatics Program Package for the Analysis, Mining, and Modeling of Chemical and Materials Data},
doi = {10.26434/chemrxiv.8323271.v1},
year = {2019}
}


Other references:

@article{chemml_review2019,
author = {Haghighatlari, Mojtaba and Hachmann, Johannes},
doi = {https://doi.org/10.1016/j.coche.2019.02.009},
issn = {2211-3398},
journal = {Current Opinion in Chemical Engineering},
month = {jan},
pages = {51--57},
title = {Advances of machine learning in molecular modeling and simulation},
volume = {23},
year = {2019}
}

@article{Hachmann2018,
author = {Hachmann, Johannes and Afzal, Mohammad Atif Faiz and Haghighatlari, Mojtaba and Pal, Yudhajit},
doi = {10.1080/08927022.2018.1471692},
issn = {10290435},
journal = {Molecular Simulation},
number = {11},
pages = {921--929},
title = {Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space},
volume = {44},
year = {2018}
}

License:

About us:

Maintainers:

- Johannes Hachmann, hachmann@buffalo.edu
- Mojtaba Haghighatlari
University at Buffalo - The State University of New York (UB)

Contributors:

- Doaa Altarawy (MolSSI): scientific advice and software mentor 
- Gaurav Vishwakarma (UB): automated model optimization
- Ramachandran Subramanian (UB): Magpie descriptor library port
- Bhargava Urala Kota (UB): library database
- Aditya Sonpal (UB): debugging
- Srirangaraj Setlur (UB): scientific advice
- Venugopal Govindaraju (UB): scientific advice
- Krishna Rajan (UB): scientific advice

- We encourage any contributions and feedback. Feel free to fork and make pull-request to the "development" branch.

Acknowledgements:

- ChemML is based upon work supported by the U.S. National Science Foundation under grant #OAC-1751161 and in part by #OAC-1640867.
- ChemML was also supported by start-up funds provided by UB's School of Engineering and Applied Science and UB's Department of Chemical and Biological Engineering, the New York State Center of Excellence in Materials Informatics through seed grant #1140384-8-75163, and the U.S. Department of Energy under grant #DE-SC0017193.
- Mojtaba Haghighatlari received 2018 Phase-I and 2019 Phase-II Software Fellowships by the Molecular Sciences Software Institute (MolSSI) for his work on ChemML.

lqcata/chemml