Precise Omnidirectional Camera Calibration. - Dennis Strelow; Jeffrey Mishler; David Koes; Sanjiv Singh (CVPR (1), 2001)

Programmer specified pointer independence. - David Koes; Mihai Budiu; Girish Venkataramani (Memory System Performance, 2004)

A Progressive Register Allocator for Irregular Architectures. - David Koes; Seth Copen Goldstein (CGO, 2005)

A global progressive register allocator. - David Ryan Koes; Seth Copen Goldstein (PLDI, 2006)

Near-optimal instruction selection on dags. - David Ryan Koes; Seth Copen Goldstein (CGO, 2008)

Register allocation deconstructed. - David Ryan Koes; Seth Copen Goldstein (SCOPES, 2009)

Pharmer: Efficient and Exact Pharmacophore Search. - David Ryan Koes; Carlos J. Camacho (J. Chem. Inf. Model., 2011)

Small-molecule inhibitor starting points learned from protein-protein interaction inhibitor structure. - David Ryan Koes; Carlos J. Camacho (Bioinform., 2012)

PocketQuery: protein-protein interaction inhibitor starting points from protein-protein interaction structure. - David Ryan Koes; Carlos J. Camacho (Nucleic Acids Res., 2012)

ZINCPharmer: pharmacophore search of the ZINC database. - David Ryan Koes; Carlos J. Camacho (Nucleic Acids Res., 2012)

Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise. - David Ryan Koes; Matthew P. Baumgartner; Carlos J. Camacho (J. Chem. Inf. Model., 2013)

Shape-based virtual screening with volumetric aligned molecular shapes. - David Ryan Koes; Carlos J. Camacho (J. Comput. Chem., 2014)

3Dmol.js: molecular visualization with WebGL. - Nicholas Rego; David Koes (Bioinform., 2015)

Indexing volumetric shapes with matching and packing. - David Ryan Koes; Carlos J. Camacho (Knowl. Inf. Syst., 2015)

A D3R prospective evaluation of machine learning for protein-ligand scoring. - Jocelyn Sunseri; Matthew Ragoza; Jasmine Collins; David Ryan Koes (J. Comput. Aided Mol. Des., 2016)

Pharmit: interactive exploration of chemical space. - Jocelyn Sunseri; David Ryan Koes (Nucleic Acids Res., 2016)

Ligand Pose Optimization with Atomic Grid-Based Convolutional Neural Networks. - Matthew Ragoza; Lillian Turner; David Ryan Koes (CoRR, 2017)

Protein-Ligand Scoring with Convolutional Neural Networks. - Matthew Ragoza; Joshua E. Hochuli; Elisa Idrobo; Jocelyn Sunseri; David Ryan Koes (J. Chem. Inf. Model., 2017)

Visualizing Convolutional Neural Network Protein-Ligand Scoring. - Joshua E. Hochuli; Alec Helbling; Tamar Skaist; Matthew Ragoza; David Ryan Koes (CoRR, 2018)

The Pharmit backend: A computer systems approach to enabling interactive online drug discovery. - David Ryan Koes (IBM J. Res. Dev., 2018)

libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications. - Jocelyn Sunseri; David Ryan Koes (CoRR, 2019)

Convolutional neural network scoring and minimization in the D3R 2017 community challenge. - Jocelyn Sunseri; Jonathan E. King; Paul G. Francoeur; David Ryan Koes (J. Comput. Aided Mol. Des., 2019)

SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning. - Jonathan E. King; David Ryan Koes (CoRR, 2020)

Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models. - Matthew Ragoza; Tomohide Masuda; David Ryan Koes (CoRR, 2020)

Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models. - Tomohide Masuda; Matthew Ragoza; David Ryan Koes (CoRR, 2020)

Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design. - Paul G. Francoeur; Tomohide Masuda; Jocelyn Sunseri; Andrew Jia; Richard B. Iovanisci; Ian Snyder; David Ryan Koes (J. Chem. Inf. Model., 2020)

libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications. - Jocelyn Sunseri; David Ryan Koes (J. Chem. Inf. Model., 2020)

Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Models. - Matthew Ragoza; Tomohide Masuda; David Ryan Koes (CoRR, 2021)

GNINA 1.0: molecular docking with deep learning. - Andrew T. McNutt; Paul G. Francoeur; Rishal Aggarwal; Tomohide Masuda; Rocco Meli; Matthew Ragoza; Jocelyn Sunseri; David Ryan Koes (J. Cheminformatics, 2021)

SolTranNet-A Machine Learning Tool for Fast Aqueous Solubility Prediction. - Paul G. Francoeur; David Ryan Koes (J. Chem. Inf. Model., 2021)

Correction to "SolTranNet - A Machine Learning Tool for Fast Aqueous Solubility Prediction". - Paul G. Francoeur; David Ryan Koes (J. Chem. Inf. Model., 2021)

Improving ΔΔG Predictions with a Multitask Convolutional Siamese Network. - Andrew T. McNutt; David Ryan Koes (J. Chem. Inf. Model., 2022)

Accelerating Inference in Molecular Diffusion Models with Latent Representations of Protein Structure. - Ian Dunn; David Ryan Koes (CoRR, 2023)

Conformer Generation for Structure-Based Drug Design: How Many and How Good? - Andrew T. McNutt; Fatimah Bisiriyu; Sophia Song; Ananya Vyas; Geoffrey R. Hutchison; David Ryan Koes (J. Chem. Inf. Model., 2023)

Systematic Comparison of Experimental Crystallographic Geometries and Gas-Phase Computed Conformers for Torsion Preferences. - Dakota L. Folmsbee; David Ryan Koes; Geoffrey R. Hutchison (J. Chem. Inf. Model., 2023)

Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation. - Ian Dunn; David Ryan Koes (CoRR, 2024)

SPRINT Enables Interpretable and Ultra-Fast Virtual Screening against Thousands of Proteomes. - Andrew T. McNutt; Abhinav Adduri; Caleb N. Ellington; Monica T. Dayao; Eric P. Xing; Hosein Mohimani; David Ryan Koes (CoRR, 2024)

Exploring Discrete Flow Matching for 3D De Novo Molecule Generation. - Ian Dunn; David Ryan Koes (CoRR, 2024)

CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks. - Roshni Bhatt; David Ryan Koes; Jacob D. Durrant (J. Chem. Inf. Model., 2024)

BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening. - Michael Brocidiacono; Paul G. Francoeur; Rishal Aggarwal; Konstantin I. Popov; David Ryan Koes; Alexander Tropsha (J. Chem. Inf. Model., 2024)

CACHE Challenge #1: Docking with GNINA Is All You Need. - Ian Dunn; Somayeh Pirhadi; Yao Wang; Smmrithi Ravindran; Carter Concepcion; David Ryan Koes (J. Chem. Inf. Model., 2024)

CACHE Challenge #1: Targeting the WDR Domain of LRRK2, A Parkinson's Disease Associated Protein. - Fengling Li; Suzanne Ackloo; Cheryl H. Arrowsmith; Fuqiang Ban; Christopher J. Barden; Hartmut Beck; Jan Beránek; Francois Berenger; Albina Bolotokova; Guillaume Bret; Marko Breznik; Emanuele Carosati; et al. (J. Chem. Inf. Model., 2024)

Open-ComBind: harnessing unlabeled data for improved binding pose prediction. - Andrew T. McNutt; David Ryan Koes (J. Comput. Aided Mol. Des., 2024)

CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13. - Oleksandra Herasymenko; Madhushika Silva; Abd Al-Aziz A. Abu-Saleh; Ayaz Ahmad; Jesus Alvarado-Huayhuaz; Oscar Emilio Arrúa Arce; Roly J. Armstrong; Cheryl H. Arrowsmith; Kelly E. Bachta; Hartmut Beck; Dénes Berta; Mateusz K. Bieniek; et al. (J. Chem. Inf. Model., 2025)

GEOM-Drugs Revisited: Toward More Chemically Accurate Benchmarks for 3D Molecule Generation. - Filipp Nikitin; Ian Dunn; David Ryan Koes; Olexandr Isayev (CoRR, 2025)

BoltzNCE: Learning Likelihoods for Boltzmann Generation with Stochastic Interpolants and Noise Contrastive Estimation. - Rishal Aggarwal; Jacky Chen; Nicholas M. Boffi; David Ryan Koes (CoRR, 2025)

FlowMol3: Flow Matching for 3D De Novo Small-Molecule Generation. - Ian Dunn; David Ryan Koes (CoRR, 2025)

GNINA 1.3: the next increment in molecular docking with deep learning. - Andrew T. McNutt; Yanjing Li; Rocco Meli; Rishal Aggarwal; David Ryan Koes (J. Cheminformatics, 2025)