David Ryan Koes

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 27

Top Topics

Diffusion Models

Convolutional Neural Networks

Top Venues

J. Chem. Inf. Model.

J. Comput. Aided Mol. Des.

IBM J. Res. Dev.

Publications

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) (2024)
Andrew T. McNutt, David Ryan Koes
Open-ComBind: harnessing unlabeled data for improved binding pose prediction. J. Comput. Aided Mol. Des. 38 (1) (2024)
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) (2024)
Ian Dunn, David Ryan Koes
Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation. CoRR (2024)
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) (2023)
Ian Dunn, David Ryan Koes
Accelerating Inference in Molecular Diffusion Models with Latent Representations of Protein Structure. CoRR (2023)
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) (2023)
Andrew T. McNutt, David Ryan Koes
Predictions with a Multitask Convolutional Siamese Network. J. Chem. Inf. Model. 62 (8) (2022)
Paul G. Francoeur, David Ryan Koes
SolTranNet-A Machine Learning Tool for Fast Aqueous Solubility Prediction. J. Chem. Inf. Model. 61 (6) (2021)
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) (2021)
Paul G. Francoeur, David Ryan Koes
Correction to "SolTranNet - A Machine Learning Tool for Fast Aqueous Solubility Prediction". J. Chem. Inf. Model. 61 (8) (2021)
Matthew Ragoza, Tomohide Masuda, David Ryan Koes
Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Models. CoRR (2021)
Tomohide Masuda, Matthew Ragoza, David Ryan Koes
Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models. CoRR (2020)
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) (2020)
Jocelyn Sunseri, David Ryan Koes
libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications. J. Chem. Inf. Model. 60 (3) (2020)
Matthew Ragoza, Tomohide Masuda, David Ryan Koes
Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models. CoRR (2020)
Jonathan E. King, David Ryan Koes
SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning. CoRR (2020)
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) (2019)
Jocelyn Sunseri, David Ryan Koes
libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications. CoRR (2019)
Joshua E. Hochuli, Alec Helbling, Tamar Skaist, Matthew Ragoza, David Ryan Koes
Visualizing Convolutional Neural Network Protein-Ligand Scoring. CoRR (2018)
David Ryan Koes
The Pharmit backend: A computer systems approach to enabling interactive online drug discovery. IBM J. Res. Dev. 62 (6) (2018)
Matthew Ragoza, Lillian Turner, David Ryan Koes
Ligand Pose Optimization with Atomic Grid-Based Convolutional Neural Networks. CoRR (2017)
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) (2017)
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) (2016)
Matthew Ragoza, Joshua E. Hochuli, Elisa Idrobo, Jocelyn Sunseri, David Ryan Koes
Protein-Ligand Scoring with Convolutional Neural Networks. CoRR (2016)
Jocelyn Sunseri, David Ryan Koes
Pharmit: interactive exploration of chemical space. Nucleic Acids Res. 44 (Webserver-Issue) (2016)
David Ryan Koes, Carlos J. Camacho
Indexing volumetric shapes with matching and packing. Knowl. Inf. Syst. 43 (1) (2015)
David Ryan Koes, Carlos J. Camacho
Shape-based virtual screening with volumetric aligned molecular shapes. J. Comput. Chem. 35 (25) (2014)
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) (2013)
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) (2012)
David Ryan Koes, Carlos J. Camacho
ZINCPharmer: pharmacophore search of the ZINC database. Nucleic Acids Res. 40 (Web-Server-Issue) (2012)
David Ryan Koes, Carlos J. Camacho
Small-molecule inhibitor starting points learned from protein-protein interaction inhibitor structure. Bioinform. 28 (14) (2012)
David Ryan Koes, Carlos J. Camacho
Small-molecule inhibitor starting points learned from protein-protein interaction inhibitor structure. Bioinform. 28 (6) (2012)
David Ryan Koes, Carlos J. Camacho
Pharmer: Efficient and Exact Pharmacophore Search. J. Chem. Inf. Model. 51 (6) (2011)
David Ryan Koes, Seth Copen Goldstein
Register allocation deconstructed. SCOPES (2009)
David Ryan Koes, Seth Copen Goldstein
Near-optimal instruction selection on dags. CGO (2008)
David Ryan Koes, Seth Copen Goldstein
A global progressive register allocator. PLDI (2006)