ChemLG – A Library Generator for the Exploration and Enumeration of Chemical and Materials Space

ChemLG is a smart and massive parallel molecular library generator for chemical and materials sciences.

Github Repository: https://github.com/hachmannlab/chemlg

Program Version: 0.9

Release Date: Aug 20, 2020

With contributions by: Gaurav Vishwakarma (UB): Genetic Algorithm, Code Redesign and Maintenance Janhavi Abhay Dudwadkar (UB): Jupyter GUI

Code Design

ChemLG is developed in the Python 3 programming language and uses OpenBabel and its Python extension, Pybel for handling molecules. The development follows a strictly modular and object-oriented design to make the overall code as flexible and versatile as possible. ChemLG can be run on a single core or in parallel on multiple cores. For the parallel execution, MPI4Py is also required along with OpenBabel as dependencies of ChemLG.

Installation and Dependencies

It is highly recommended that a virtual environment is used to run ChemLG. The virtual environment and ChemLG and its dependencies can be installed as:

conda create --name my_chemlg_env python=3.6
source activate my_chemlg_env
conda install -c conda-forge openbabel
conda install -c anaconda mpi4py
pip install chemlg

ChemLG Guide

Citation

Please cite ChemLG as follows:

  • Afzal, M. A. F.; Vishwakarma, G.; Dudwadkar, J. A.;Haghighatlari, M.; Hachmann, J. ChemLG– A Library Generator for the Exploration and Enumeration of Chemical and Materials Spaces. 2019; https://github.com/hachmannlab/chemlg

  • M.A.F. Afzal, G. Vishwakarma, J. Hachmann, ChemLG – ChemLG– A Library Generator for the Exploration and Enumeration of Chemical and Materials Spaces. Available from: https://hachmannlab.github.io/chemlg

    1. Hachmann, M.A.F. Afzal, M. Haghighatlari, Y. Pal, Building and Deploying a Cyberinfrastructure for the Data-Driven Design of Chemical Systems and the Exploration of Chemical Space, Mol. Simul. 44 (2018), 921-929. DOI: 10.1080/08927022.2018.1471692

Acknowledgement

ChemLG is based upon work supported by the U.S. National Science Foundation under grant #OAC-1751161. It 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.