Abstract
This repository contains code for the computational framework to build ML models for drug discovery
purposes.
Our end-to-end pipeline extracts features from model-ready datasets and trains and saves the model to our model zoo or to disk.
Our pipeline generates a variety of molecular features and both shallow and deep ML models.
The HPC-specific module we have developed conducts efficient parallelized search of the model hyperparameter space and reports the best-performing hyperparameters for each of these feature/model combinations.
- Developers:
-
Minnich, Amanda [1] ; McLoughlin, Kevin [1] ; Allen, Jonathan [1] ; Forsyth, Ryan [1]
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Contributors:
-
Researcher: Weber, Claire [1] ; Murad, Neha [1] ; Tse, Margaret [1] ; Weber, Andrew [1] ; Deng, Jason [1] ; Madej, Ben [2] - GlaxoSmithKline plc (GSK)
- Frederick National Laboratory
- Contributing Organizations:
-
Research Group: Frederick National Laboratory for Cancer Research Research Group: GlaxoSmithKline plc (GSK)
- Release Date:
- 2019-10-26
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Version:
- 1.0
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE National Nuclear Security Administration (NNSA)Primary Award/Contract Number:AC52-07NA27344
- Code ID:
- 32934
- Site Accession Number:
- 997148
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Country of Origin:
- United States
Citation Formats
Minnich, Amanda J., McLoughlin, Kevin S., Allen, Jonathan E., Forsyth, Ryan M., Weber, Claire, Murad, Neha, Tse, Margaret, Weber, Andrew, Deng, Jason, and Madej, Ben.
Atom Modeling PipeLine.
Computer Software.
https://github.com/ATOMconsortium/AMPL.
USDOE National Nuclear Security Administration (NNSA).
26 Oct. 2019.
Web.
doi:10.11578/dc.20191211.1.
Minnich, Amanda J., McLoughlin, Kevin S., Allen, Jonathan E., Forsyth, Ryan M., Weber, Claire, Murad, Neha, Tse, Margaret, Weber, Andrew, Deng, Jason, & Madej, Ben.
(2019, October 26).
Atom Modeling PipeLine.
[Computer software].
https://github.com/ATOMconsortium/AMPL.
https://doi.org/10.11578/dc.20191211.1.
Minnich, Amanda J., McLoughlin, Kevin S., Allen, Jonathan E., Forsyth, Ryan M., Weber, Claire, Murad, Neha, Tse, Margaret, Weber, Andrew, Deng, Jason, and Madej, Ben.
"Atom Modeling PipeLine." Computer software.
October 26, 2019.
https://github.com/ATOMconsortium/AMPL.
https://doi.org/10.11578/dc.20191211.1.
@misc{
doecode_32934,
title = {Atom Modeling PipeLine},
author = {Minnich, Amanda J. and McLoughlin, Kevin S. and Allen, Jonathan E. and Forsyth, Ryan M. and Weber, Claire and Murad, Neha and Tse, Margaret and Weber, Andrew and Deng, Jason and Madej, Ben},
abstractNote = {This repository contains code for the computational framework to build ML models for drug discovery
purposes.
Our end-to-end pipeline extracts features from model-ready datasets and trains and saves the model to our model zoo or to disk.
Our pipeline generates a variety of molecular features and both shallow and deep ML models.
The HPC-specific module we have developed conducts efficient parallelized search of the model hyperparameter space and reports the best-performing hyperparameters for each of these feature/model combinations.},
doi = {10.11578/dc.20191211.1},
url = {https://doi.org/10.11578/dc.20191211.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20191211.1}},
year = {2019},
month = {oct}
}