publications

2022

1. Accelerators for Classical Molecular Dynamics Simulations of Biomolecules

   Jones, Derek, Jonathan, Allen E, Yang, Yue, Bennett, W F D, Gokhale, Maya, Moshiri, Niema, and Rosing, Tajana

   J. Chem. Theory Comput. 2022

   Bib

   @article{Jones_2022-uq,
     title = {Accelerators for Classical Molecular Dynamics Simulations of
                  Biomolecules},
     author = {Jones, Derek and Jonathan, Allen E and Yang, Yue and Bennett, W F D and Gokhale, Maya and Moshiri, Niema and Rosing, Tajana},
     journal = {J. Chem. Theory Comput.},
     volume = {},
     number = {},
     pages = {},
     month = {},
     year = {2022},
     language = {en},
     selected = {true},
     bibtex_show = true
   }

2021

1. Informing deep learning methodologies for drug discovery with biophysical simulation

   Jones, Derek

   2021

   Bib

   @booklet{jones289_acs_fall_meet_2021,
     author = {Jones, Derek},
     title = {Informing deep learning methodologies for drug discovery with biophysical simulation},
     howpublished = {Deep Learning For Chemistry: Resilience of Methods & Workflows at ACS Fall Meeting 2021},
     year = {2021},
     bibtex_show = true
   }

2. Accelerators for Classical Molecular Dynamics Simulations of Biomolecules (Invited Talk)

   Jones, Derek

   2021

   Bib

   @booklet{jones289_biosec_talk,
     author = {Jones, Derek},
     title = {Accelerators for Classical Molecular Dynamics Simulations of Biomolecules (Invited Talk)},
     howpublished = {LLNL Biosecurity Center Seminar},
     year = {2021},
     bibtex_show = true
   }

3. Improved Protein-Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference

   Jones, Derek, Kim, Hyojin, Zhang, Xiaohua, Zemla, Adam, Stevenson, Garrett, Bennett, W F Drew, Kirshner, Daniel, Wong, Sergio E, Lightstone, Felice C, and Allen, Jonathan E

   J. Chem. Inf. Model. 2021

   Bib

   @article{Jones2021-al,
     title = {Improved {Protein-Ligand} Binding Affinity Prediction with
                   {Structure-Based} Deep Fusion Inference},
     author = {Jones, Derek and Kim, Hyojin and Zhang, Xiaohua and Zemla, Adam and Stevenson, Garrett and Bennett, W F Drew and Kirshner, Daniel and Wong, Sergio E and Lightstone, Felice C and Allen, Jonathan E},
     journal = {J. Chem. Inf. Model.},
     volume = {61},
     number = {4},
     pages = {1583--1592},
     month = apr,
     year = {2021},
     language = {en},
     selected = {true},
     bibtex_show = true
   }

4. Enabling rapid COVID-19 small molecule drug design through scalable deep learning of generative models

   Jacobs, Sam Ade, Moon, Tim, McLoughlin, Kevin, Jones, Derek, Hysom, David, Ahn, Dong H, Gyllenhaal, John, Watson, Pythagoras, Lightstone, Felice C, Allen, Jonathan E, Karlin, Ian, and Van Essen, Brian

   Int. J. High Perform. Comput. Appl. 2021

   Bib

   @article{Jacobs2021-ag,
     title = {Enabling rapid {COVID-19} small molecule drug design through
                    scalable deep learning of generative models},
     author = {Jacobs, Sam Ade and Moon, Tim and McLoughlin, Kevin and Jones, Derek and Hysom, David and Ahn, Dong H and Gyllenhaal, John and Watson, Pythagoras and Lightstone, Felice C and Allen, Jonathan E and Karlin, Ian and Van Essen, Brian},
     journal = {Int. J. High Perform. Comput. Appl.},
     publisher = {SAGE Publications Ltd STM},
     volume = {35},
     number = {5},
     pages = {469--482},
     month = sep,
     year = {2021},
     bibtex_show = true
   }

5. High-throughput virtual screening of small molecule inhibitors for SARS-CoV-2 protein targets with deep fusion models

   Stevenson, Garrett A, Jones, Derek, Kim, Hyojin, Bennett, W F Drew, Bennion, Brian J, Borucki, Monica, Bourguet, Feliza, Epstein, Aidan, Franco, Magdalena, Harmon, Brooke, He, Stewart, Katz, Max P, Kirshner, Daniel, Lao, Victoria, Lau, Edmond Y, Lo, Jacky, McLoughlin, Kevin, Mosesso, Richard, Murugesh, Deepa K, Negrete, Oscar A, Saada, Edwin A, Segelke, Brent, Stefan, Maxwell, Torres, Marisa W, Weilhammer, Dina, Wong, Sergio, Yang, Yue, Zemla, Adam, Zhang, Xiaohua, Zhu, Fangqiang, Lightstone, Felice C, and Allen, Jonathan E

   In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis 2021

   Bib

   @inproceedings{Stevenson2021-nw,
     title = {High-throughput virtual screening of small molecule inhibitors
                    for {SARS-CoV-2} protein targets with deep fusion models},
     booktitle = {Proceedings of the International Conference for High Performance
                    Computing, Networking, Storage and Analysis},
     author = {Stevenson, Garrett A and Jones, Derek and Kim, Hyojin and Bennett, W F Drew and Bennion, Brian J and Borucki, Monica and Bourguet, Feliza and Epstein, Aidan and Franco, Magdalena and Harmon, Brooke and He, Stewart and Katz, Max P and Kirshner, Daniel and Lao, Victoria and Lau, Edmond Y and Lo, Jacky and McLoughlin, Kevin and Mosesso, Richard and Murugesh, Deepa K and Negrete, Oscar A and Saada, Edwin A and Segelke, Brent and Stefan, Maxwell and Torres, Marisa W and Weilhammer, Dina and Wong, Sergio and Yang, Yue and Zemla, Adam and Zhang, Xiaohua and Zhu, Fangqiang and Lightstone, Felice C and Allen, Jonathan E},
     publisher = {Association for Computing Machinery},
     number = {Article 74},
     pages = {1--13},
     series = {SC '21},
     month = nov,
     year = {2021},
     address = {New York, NY, USA},
     keywords = {HPC, deep learning, SARS-CoV-2, AI, hyper-parameter
                    optimization, GPU, COVID-19},
     location = {St. Louis, Missouri},
     bibtex_show = true
   }

6. Discovery of Small-Molecule Inhibitors of SARS-CoV-2 Proteins Using a Computational and Experimental Pipeline

   Lau, Edmond Y, Negrete, Oscar A, Bennett, W F Drew, Bennion, Brian J, Borucki, Monica, Bourguet, Feliza, Epstein, Aidan, Franco, Magdalena, Harmon, Brooke, He, Stewart, Jones, Derek, Kim, Hyojin, Kirshner, Daniel, Lao, Victoria, Lo, Jacky, McLoughlin, Kevin, Mosesso, Richard, Murugesh, Deepa K, Saada, Edwin A, Segelke, Brent, Stefan, Maxwell A, Stevenson, Garrett A, Torres, Marisa W, Weilhammer, Dina R, Wong, Sergio, Yang, Yue, Zemla, Adam, Zhang, Xiaohua, Zhu, Fangqiang, Allen, Jonathan E, and Lightstone, Felice C

   Front Mol Biosci 2021

   Bib

   @article{Lau2021-dr,
     title = {Discovery of {Small-Molecule} Inhibitors of {SARS-CoV-2} Proteins
                   Using a Computational and Experimental Pipeline},
     author = {Lau, Edmond Y and Negrete, Oscar A and Bennett, W F Drew and Bennion, Brian J and Borucki, Monica and Bourguet, Feliza and Epstein, Aidan and Franco, Magdalena and Harmon, Brooke and He, Stewart and Jones, Derek and Kim, Hyojin and Kirshner, Daniel and Lao, Victoria and Lo, Jacky and McLoughlin, Kevin and Mosesso, Richard and Murugesh, Deepa K and Saada, Edwin A and Segelke, Brent and Stefan, Maxwell A and Stevenson, Garrett A and Torres, Marisa W and Weilhammer, Dina R and Wong, Sergio and Yang, Yue and Zemla, Adam and Zhang, Xiaohua and Zhu, Fangqiang and Allen, Jonathan E and Lightstone, Felice C},
     journal = {Front Mol Biosci},
     volume = {8},
     pages = {678701},
     month = jul,
     year = {2021},
     keywords = {COVID-19; FRET; live virus; machine-learning; main protease;
                   molecular simulations; protein assays; spike protein},
     language = {en},
     bibtex_show = true
   }

2020

1. Predicting Small Molecule Transfer Free Energies by Combining Molecular Dynamics Simulations and Deep Learning

   Bennett, W F Drew, He, Stewart, Bilodeau, Camille L, Jones, Derek, Sun, Delin, Kim, Hyojin, Allen, Jonathan E, Lightstone, Felice C, and Ingólfsson, Helgi I

   J. Chem. Inf. Model. 2020

   Bib

   @article{Bennett2020-rc,
     title = {Predicting Small Molecule Transfer Free Energies by Combining
                   Molecular Dynamics Simulations and Deep Learning},
     author = {Bennett, W F Drew and He, Stewart and Bilodeau, Camille L and Jones, Derek and Sun, Delin and Kim, Hyojin and Allen, Jonathan E and Lightstone, Felice C and Ing{\'o}lfsson, Helgi I},
     journal = {J. Chem. Inf. Model.},
     volume = {60},
     number = {11},
     pages = {5375--5381},
     month = nov,
     year = {2020},
     language = {en},
     bibtex_show = true
   }

2. Binding Affinity Prediction by Pairwise Function Based on Neural Network

   Zhu, Fangqiang, Zhang, Xiaohua, Allen, Jonathan E, Jones, Derek, and Lightstone, Felice C

   J. Chem. Inf. Model. 2020

   Bib

   @article{Zhu2020-ge,
     title = {Binding Affinity Prediction by Pairwise Function Based on Neural
                   Network},
     author = {Zhu, Fangqiang and Zhang, Xiaohua and Allen, Jonathan E and Jones, Derek and Lightstone, Felice C},
     journal = {J. Chem. Inf. Model.},
     volume = {60},
     number = {6},
     pages = {2766--2772},
     month = jun,
     year = {2020},
     language = {en},
     bibtex_show = true
   }

2019

1. Fusion of Structure Based Deep Learning to Accelerate Molecular Docking Predictions

   Jones, Derek

   2019

   Bib

   @booklet{jones289_fusion_ml,
     title = {Fusion of Structure Based Deep Learning to Accelerate Molecular Docking Predictions},
     author = {Jones, Derek},
     year = {2019},
     howpublished = {5th Computational Approaches for Cancer Workshop at SC19},
     url = {},
     bibtex_show = true
   }

2. Leveraging Large Scale Molecular Dynamics Simulations and Deep Learning for Binding Activity Models

   Jones, Derek

   2019

   Bib

   @booklet{jones289_LargeScaleMD,
     title = {Leveraging Large Scale Molecular Dynamics Simulations and Deep Learning for Binding Activity Models},
     author = {Jones, Derek},
     year = {2019},
     howpublished = {SIAM 2019 Conference on Computational Science and Engineering},
     url = {},
     bibtex_show = true
   }

2018

1. Incorporating Protein Dynamics Through Ensemble Docking in Machine Learning Models to Predict Drug Binding

   Alghamedy, Fatemah, Bopaiah, Jeevith, Jones, Derek, Zhang, Xiaofei, Weiss, Heidi L, and Ellingson, Sally R

   AMIA Jt Summits Transl Sci Proc 2018

   Bib

   @article{Alghamedy2018-pj,
     title = {Incorporating Protein Dynamics Through Ensemble Docking in
                   Machine Learning Models to Predict Drug Binding},
     author = {Alghamedy, Fatemah and Bopaiah, Jeevith and Jones, Derek and Zhang, Xiaofei and Weiss, Heidi L and Ellingson, Sally R},
     journal = {AMIA Jt Summits Transl Sci Proc},
     volume = {2017},
     pages = {26--34},
     month = may,
     year = {2018},
     language = {en},
     bibtex_show = true
   }

2. Polypharmacology Within the Full Kinome: a Machine Learning Approach

   Jones, Derek, Bopaiah, Jeevith, Alghamedy, Fatemah, Jacobs, Nathan, Weiss, Heidi L, Jong, W A, and Ellingson, Sally R

   AMIA Jt Summits Transl Sci Proc 2018

   Bib

   @article{Jones2018-ci,
     title = {Polypharmacology Within the Full Kinome: a Machine Learning
                   Approach},
     author = {Jones, Derek and Bopaiah, Jeevith and Alghamedy, Fatemah and Jacobs, Nathan and Weiss, Heidi L and de Jong, W A and Ellingson, Sally R},
     journal = {AMIA Jt Summits Transl Sci Proc},
     volume = {2017},
     pages = {98--107},
     month = may,
     year = {2018},
     language = {en},
     bibtex_show = true
   }