Jonathan E. Allen

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 21

Top Topics

Molecular Dynamics

Top Venues

J. Chem. Inf. Model.

Briefings Bioinform.

Publications

Heesung Shim, Jonathan E. Allen, W. F. Drew Bennett
Enhancing Docking Accuracy with PECAN2, a 3D Atomic Neural Network Trained without Co-Complex Crystal Structures. Mach. Learn. Knowl. Extr. 6 (1) (2024)
Derek Jones, Jonathan E. Allen, Xiaohua Zhang, Behnam Khaleghi, Jaeyoung Kang, Weihong Xu, Niema Moshiri, Tajana Simunic Rosing
HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations. CoRR (2023)
Garrett A. Stevenson, Dan Kirshner, Brian J. Bennion, Yue Yang, Xiaohua Zhang, Adam T. Zemla, Marisa W. Torres, Aidan Epstein, Derek Jones, Hyojin Kim, W. F. Drew Bennett, Sergio Ernesto Wong, Jonathan E. Allen, Felice C. Lightstone
Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method. J. Chem. Inf. Model. 63 (21) (2023)
Fangfang Xia, Jonathan E. Allen, Prasanna Balaprakash, Thomas S. Brettin, Cristina Garcia-Cardona, Austin Clyde, Judith D. Cohn, James H. Doroshow, Xiaotian Duan, Veronika Dubinkina, Yvonne A. Evrard, Ya Ju Fan, Jason Gans, Stewart He, Pinyi Lu, Sergei Maslov, Alexander Partin, Maulik Shukla, Eric A. Stahlberg, Justin M. Wozniak, Hyun Seung Yoo, George F. Zaki, Yitan Zhu, Rick Stevens
A cross-study analysis of drug response prediction in cancer cell lines. Briefings Bioinform. 23 (1) (2022)
Vincent Blay, Tijana Radivojevic, Jonathan E. Allen, Corey M. Hudson, Héctor García Martín
MACAW: An Accessible Tool for Molecular Embedding and Inverse Molecular Design. J. Chem. Inf. Model. 62 (15) (2022)
Heesung Shim, Hyojin Kim, Jonathan E. Allen, Heike Wulff
Pose Classification Using Three-Dimensional Atomic Structure-Based Neural Networks Applied to Ion Channel-Ligand Docking. J. Chem. Inf. Model. 62 (10) (2022)
Ya Ju Fan, Jonathan E. Allen, Kevin S. McLoughlin, Da Shi, Brian J. Bennion, Xiaohua Zhang, Felice C. Lightstone
Evaluating Point-Prediction Uncertainties in Neural Networks for Drug Discovery. CoRR (2022)
Dong H. Ahn, Xiaohua Zhang, Jeffrey Mast, Stephen Herbein, Francesco Di Natale, Dan Kirshner, Sam Ade Jacobs, Ian Karlin, Daniel J. Milroy, Bronis R. de Supinski, Brian Van Essen, Jonathan E. Allen, Felice C. Lightstone
Scalable Composition and Analysis Techniques for Massive Scientific Workflows. e-Science (2022)
Sam Ade Jacobs, Tim Moon, Kevin McLoughlin, Derek Jones, David Hysom, Dong H. Ahn, John C. Gyllenhaal, Pythagoras Watson, Felice C. Lightstone, Jonathan E. Allen, Ian Karlin, Brian Van Essen
Enabling rapid COVID-19 small molecule drug design through scalable deep learning of generative models. Int. J. High Perform. Comput. Appl. 35 (5) (2021)
Garrett A. Stevenson, Derek Jones, Hyojin Kim, W. F. Drew Bennett, Brian J. Bennion, Monica Borucki, Feliza Bourguet, Aidan Epstein, Magdalena Franco, Brooke Harmon, Stewart He, Max P. Katz, Daniel A. Kirshner, Victoria Lao, Edmond Y. Lau, Jacky Lo, Kevin McLoughlin, Richard Mosesso, Deepa K. Murugesh, Oscar A. Negrete, Edwin A. Saada, Brent Segelke, Maxwell Stefan, Marisa W. Torres, Dina Weilhammer, Sergio E. Wong, Yue Yang, Adam T. Zemla, Xiaohua Zhang, Fangqiang Zhu, Felice C. Lightstone, Jonathan E. Allen
High-throughput virtual screening of small molecule inhibitors for SARS-CoV-2 protein targets with deep fusion models. SC (2021)
Garrett A. Stevenson, Derek Jones, Hyojin Kim, W. F. Drew Bennett, Brian J. Bennion, Monica Borucki, Feliza Bourguet, Aidan Epstein, Magdalena Franco, Brooke Harmon, Stewart He, Max P. Katz, Daniel A. Kirshner, Victoria Lao, Edmond Y. Lau, Jacky Lo, Kevin McLoughlin, Richard Mosesso, Deepa K. Murugesh, Oscar A. Negrete, Edwin A. Saada, Brent Segelke, Maxwell Stefan, Marisa W. Torres, Dina Weilhammer, Sergio E. Wong, Yue Yang, Adam T. Zemla, Xiaohua Zhang, Fangqiang Zhu, Felice C. Lightstone, Jonathan E. Allen
High-Throughput Virtual Screening of Small Molecule Inhibitors for SARS-CoV-2 Protein Targets with Deep Fusion Models. CoRR (2021)
Derek Jones, Hyojin Kim, Xiaohua Zhang, Adam T. Zemla, Garrett Stevenson, W. F. Drew Bennett, Daniel A. Kirshner, Sergio E. Wong, Felice C. Lightstone, Jonathan E. Allen
Improved Protein-Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference. J. Chem. Inf. Model. 61 (4) (2021)
Kevin McLoughlin, Claire G. Jeong, Thomas D. Sweitzer, Amanda J. Minnich, Margaret Tse, Brian J. Bennion, Jonathan E. Allen, Stacie Calad-Thomson, Tom Rush, Jim Brase
Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump. J. Chem. Inf. Model. 61 (2) (2021)
Amanda J. Minnich, Kevin McLoughlin, Margaret Tse, Jason Deng, Andrew Weber, Neha Murad, Benjamin D. Madej, Bharath Ramsundar, Tom Rush, Stacie Calad-Thomson, Jim Brase, Jonathan E. Allen
AMPL: A Data-Driven Modeling Pipeline for Drug Discovery. J. Chem. Inf. Model. 60 (4) (2020)
Derek Jones, Hyojin Kim, Xiaohua Zhang, Adam T. Zemla, Garrett Stevenson, William D. Bennett, Daniel A. Kirshner, Sergio E. Wong, Felice C. Lightstone, Jonathan E. Allen
Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference. CoRR (2020)
Brian Davis, Kevin McLoughlin, Jonathan E. Allen, Sally R. Ellingson
Quantifying Overfitting Potential in Drug Binding Datasets. ICCS (3) (2020)
W. F. Drew Bennett, Stewart He, Camille L. Bilodeau, Derek Jones, Delin Sun, Hyojin Kim, Jonathan E. Allen, Felice C. Lightstone, Helgi I. Ingólfsson
Predicting Small Molecule Transfer Free Energies by Combining Molecular Dynamics Simulations and Deep Learning. J. Chem. Inf. Model. 60 (11) (2020)
Fangqiang Zhu, Xiaohua Zhang, Jonathan E. Allen, Derek Jones, Felice C. Lightstone
Binding Affinity Prediction by Pairwise Function Based on Neural Network. J. Chem. Inf. Model. 60 (6) (2020)
Amanda J. Minnich, Kevin McLoughlin, Margaret Tse, Jason Deng, Andrew Weber, Neha Murad, Benjamin D. Madej, Bharath Ramsundar, Tom Rush, Stacie Calad-Thomson, Jim Brase, Jonathan E. Allen
AMPL: A Data-Driven Modeling Pipeline for Drug Discovery. CoRR (2019)
Ya Ju Fan, Jonathan E. Allen, Sam Ade Jacobs, Brian C. Van Essen
Distinguishing between Normal and Cancer Cells Using Autoencoder Node Saliency. CoRR (2019)
Fangfang Xia, Maulik Shukla, Thomas S. Brettin, Cristina Garcia-Cardona, Judith D. Cohn, Jonathan E. Allen, Sergei Maslov, Susan L. Holbeck, James H. Doroshow, Yvonne A. Evrard, Eric A. Stahlberg, Rick L. Stevens
Predicting tumor cell line response to drug pairs with deep learning. BMC Bioinform. (18) (2018)
Sasha Ames, Jonathan E. Allen, David A. Hysom, G. Scott Lloyd, Maya B. Gokhale
Design and Optimization of a Metagenomics Analysis Workflow for NVRAM. IPDPS Workshops (2014)
Sasha Ames, David A. Hysom, Shea N. Gardner, G. Scott Lloyd, Maya B. Gokhale, Jonathan E. Allen
Scalable metagenomic taxonomy classification using a reference genome database. Bioinform. 29 (18) (2013)
Jonathan E. Allen, Steven L. Salzberg
A phylogenetic generalized hidden Markov model for predicting alternatively spliced exons. Algorithms Mol. Biol. 1 (2006)
Jonathan E. Allen, Steven Salzberg
JIGSAW: integration of multiple sources of evidence for gene prediction. Bioinform. 21 (18) (2005)