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ORCIDDavid Ryan Koes
Person information
- affiliation: University of Pittsburgh, PA, USA
- affiliation (former): Carnegie Mellon University, Pittsburgh, USA
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Journal Articles
- 2024
[j25]Andrew T. McNutt, David Ryan Koes:
Open-ComBind: harnessing unlabeled data for improved binding pose prediction. J. Comput. Aided Mol. Des. 38(1): 3 (2024)- [j24]Michael Brocidiacono
, Paul G. Francoeur, Rishal Aggarwal, Konstantin I. Popov, David Ryan Koes, Alexander Tropsha:
BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening. J. Chem. Inf. Model. 64(7): 2488-2495 (2024) - [j23]Roshni Bhatt, David Ryan Koes, Jacob D. Durrant:
CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks. J. Chem. Inf. Model. 64(12): 4651-4660 (2024) - 2023
- [j22]Andrew T. McNutt, Fatimah Bisiriyu, Sophia Song, Ananya Vyas, Geoffrey R. Hutchison, David Ryan Koes:
Conformer Generation for Structure-Based Drug Design: How Many and How Good? J. Chem. Inf. Model. 63(21): 6598-6607 (2023) - [j21]Dakota L. Folmsbee, David Ryan Koes, Geoffrey R. Hutchison:
Systematic Comparison of Experimental Crystallographic Geometries and Gas-Phase Computed Conformers for Torsion Preferences. J. Chem. Inf. Model. 63(23): 7401-7411 (2023) - 2022
- [j20]Andrew T. McNutt, David Ryan Koes:
Improving ΔΔG Predictions with a Multitask Convolutional Siamese Network. J. Chem. Inf. Model. 62(8): 1819-1829 (2022) - 2021
- [j19]Andrew T. McNutt, Paul G. Francoeur, Rishal Aggarwal, Tomohide Masuda, Rocco Meli, Matthew Ragoza, Jocelyn Sunseri, David Ryan Koes:
GNINA 1.0: molecular docking with deep learning. J. Cheminformatics 13(1): 43 (2021) - [j18]Paul G. Francoeur, David Ryan Koes:
SolTranNet-A Machine Learning Tool for Fast Aqueous Solubility Prediction. J. Chem. Inf. Model. 61(6): 2530-2536 (2021) - [j17]Paul G. Francoeur, David Ryan Koes:
Correction to "SolTranNet - A Machine Learning Tool for Fast Aqueous Solubility Prediction". J. Chem. Inf. Model. 61(8): 4120-4123 (2021) - 2020
- [j16]Jocelyn Sunseri, David Ryan Koes:
libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications. J. Chem. Inf. Model. 60(3): 1079-1084 (2020) - [j15]Paul G. Francoeur, Tomohide Masuda, Jocelyn Sunseri, Andrew Jia, Richard B. Iovanisci, Ian Snyder, David Ryan Koes:
Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design. J. Chem. Inf. Model. 60(9): 4200-4215 (2020) - 2019
- [j14]Jocelyn Sunseri, Jonathan E. King, Paul G. Francoeur, David Ryan Koes:
Convolutional neural network scoring and minimization in the D3R 2017 community challenge. J. Comput. Aided Mol. Des. 33(1): 19-34 (2019) - 2018
- [j13]David Ryan Koes:
The Pharmit backend: A computer systems approach to enabling interactive online drug discovery. IBM J. Res. Dev. 62(6): 3:1-3:6 (2018) - 2017
- [j12]Matthew Ragoza, Joshua E. Hochuli, Elisa Idrobo, Jocelyn Sunseri, David Ryan Koes:
Protein-Ligand Scoring with Convolutional Neural Networks. J. Chem. Inf. Model. 57(4): 942-957 (2017) - 2016
- [j11]Jocelyn Sunseri, Matthew Ragoza, Jasmine Collins, David Ryan Koes:
A D3R prospective evaluation of machine learning for protein-ligand scoring. J. Comput. Aided Mol. Des. 30(9): 761-771 (2016) - [j10]Jocelyn Sunseri, David Ryan Koes:
Pharmit: interactive exploration of chemical space. Nucleic Acids Res. 44(Webserver-Issue): W442-W448 (2016) - 2015
- [j9]Nicholas Rego, David Koes:
3Dmol.js: molecular visualization with WebGL. Bioinform. 31(8): 1322-1324 (2015) - [j8]David Ryan Koes, Carlos J. Camacho:
Indexing volumetric shapes with matching and packing. Knowl. Inf. Syst. 43(1): 157-180 (2015) - 2014
- [j7]David Ryan Koes, Carlos J. Camacho:
Shape-based virtual screening with volumetric aligned molecular shapes. J. Comput. Chem. 35(25): 1824-1834 (2014) - 2013
- [j6]David Ryan Koes, Matthew P. Baumgartner, Carlos J. Camacho:
Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise. J. Chem. Inf. Model. 53(8): 1893-1904 (2013) - 2012
- [j5]David Ryan Koes, Carlos J. Camacho:
Small-molecule inhibitor starting points learned from protein-protein interaction inhibitor structure. Bioinform. 28(6): 784-791 (2012) - [j4]David Ryan Koes, Carlos J. Camacho:
Small-molecule inhibitor starting points learned from protein-protein interaction inhibitor structure. Bioinform. 28(14): 1951 (2012) - [j3]David Ryan Koes, Carlos J. Camacho:
PocketQuery: protein-protein interaction inhibitor starting points from protein-protein interaction structure. Nucleic Acids Res. 40(Web-Server-Issue): 387-392 (2012) - [j2]David Ryan Koes, Carlos J. Camacho:
ZINCPharmer: pharmacophore search of the ZINC database. Nucleic Acids Res. 40(Web-Server-Issue): 409-414 (2012) - 2011
- [j1]David Ryan Koes, Carlos J. Camacho:
Pharmer: Efficient and Exact Pharmacophore Search. J. Chem. Inf. Model. 51(6): 1307-1314 (2011)
Conference and Workshop Papers
- 2009
- [c6]David Ryan Koes, Seth Copen Goldstein:
Register allocation deconstructed. SCOPES 2009: 21-30 - 2008
- [c5]David Ryan Koes, Seth Copen Goldstein:
Near-optimal instruction selection on dags. CGO 2008: 45-54 - 2006
- [c4]David Ryan Koes, Seth Copen Goldstein:
A global progressive register allocator. PLDI 2006: 204-215 - 2005
- [c3]David Koes, Seth Copen Goldstein:
A Progressive Register Allocator for Irregular Architectures. CGO 2005: 269-280 - 2004
- [c2]David Koes, Mihai Budiu, Girish Venkataramani:
Programmer specified pointer independence. Memory System Performance 2004: 51-59 - 2001
- [c1]Dennis Strelow, Jeffrey Mishler, David Koes, Sanjiv Singh:
Precise Omnidirectional Camera Calibration. CVPR (1) 2001: 689-694
Informal and Other Publications
- 2024
- [i10]Ian Dunn, David Ryan Koes:
Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation. CoRR abs/2404.19739 (2024) - 2023
- [i9]Ian Dunn, David Ryan Koes:
Accelerating Inference in Molecular Diffusion Models with Latent Representations of Protein Structure. CoRR abs/2311.13466 (2023) - 2021
- [i8]Matthew Ragoza, Tomohide Masuda, David Ryan Koes:
Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Models. CoRR abs/2110.15200 (2021) - 2020
- [i7]Jonathan E. King, David Ryan Koes:
SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning. CoRR abs/2010.08162 (2020) - [i6]Matthew Ragoza, Tomohide Masuda, David Ryan Koes:
Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models. CoRR abs/2010.08687 (2020) - [i5]Tomohide Masuda, Matthew Ragoza, David Ryan Koes:
Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models. CoRR abs/2010.14442 (2020) - 2019
- [i4]Jocelyn Sunseri, David Ryan Koes:
libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications. CoRR abs/1912.04822 (2019) - 2018
- [i3]Joshua E. Hochuli, Alec Helbling, Tamar Skaist, Matthew Ragoza, David Ryan Koes:
Visualizing Convolutional Neural Network Protein-Ligand Scoring. CoRR abs/1803.02398 (2018) - 2017
- [i2]Matthew Ragoza, Lillian Turner, David Ryan Koes:
Ligand Pose Optimization with Atomic Grid-Based Convolutional Neural Networks. CoRR abs/1710.07400 (2017) - 2016
- [i1]Matthew Ragoza, Joshua E. Hochuli, Elisa Idrobo, Jocelyn Sunseri, David Ryan Koes:
Protein-Ligand Scoring with Convolutional Neural Networks. CoRR abs/1612.02751 (2016)
Coauthor Index
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