| Protein–ligand scoring with convolutional neural networks M Ragoza, J Hochuli, E Idrobo, J Sunseri, DR Koes Journal of chemical information and modeling 57 (4), 942-957, 2017 | 861 | 2017 |
| GNINA 1.0: molecular docking with deep learning AT McNutt, P Francoeur, R Aggarwal, T Masuda, R Meli, M Ragoza, ... Journal of cheminformatics 13 (1), 43, 2021 | 488 | 2021 |
| Pharmit: interactive exploration of chemical space J Sunseri, DR Koes Nucleic acids research 44 (W1), W442-W448, 2016 | 354 | 2016 |
| Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design PG Francoeur, T Masuda, J Sunseri, A Jia, RB Iovanisci, I Snyder, ... Journal of chemical information and modeling 60 (9), 4200-4215, 2020 | 270 | 2020 |
| Open source molecular modeling S Pirhadi, J Sunseri, DR Koes Journal of Molecular Graphics and Modelling 69, 127-143, 2016 | 118 | 2016 |
| Convolutional neural network scoring and minimization in the D3R 2017 community challenge J Sunseri, JE King, PG Francoeur, DR Koes Journal of computer-aided molecular design 33, 19-34, 2019 | 60 | 2019 |
| Libmolgrid: graphics processing unit accelerated molecular gridding for deep learning applications J Sunseri, DR Koes Journal of chemical information and modeling 60 (3), 1079-1084, 2020 | 57 | 2020 |
| Virtual Screening with Gnina 1.0 J Sunseri, DR Koes Molecules 26 (23), 7369, 2021 | 42 | 2021 |
| A D3R prospective evaluation of machine learning for protein-ligand scoring J Sunseri, M Ragoza, J Collins, DR Koes Journal of computer-aided molecular design 30, 761-771, 2016 | 21 | 2016 |
| 3D Convolutional Neural Networks for Computational Drug Discovery J Sunseri University of Pittsburgh, 2021 | 1 | 2021 |
| 3D Convolutional neural networks and a crossdocked dataset for structure-based drug design P Francoeur, T Masuda, J Sunseri, A Jia, RB Iovanisci, I Snyder, DR Koes J Chem Inf Model, 2020 | | 2020 |
| Recurrent neural network modeling of molecular data sequentialized in time and space J Sunseri, D Koes ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 257, 2019 | | 2019 |
| Versatile grid-based molecular input library optimized for machine learning J Sunseri, D Koes ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 257, 2019 | | 2019 |
| GPU molecular docking with convolutional neural network scoring functions J Sunseri, D Koes ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 255, 2018 | | 2018 |
| GPU implementation of molecular docking with applications to receptor flexibility and energy landscape sampling J Sunseri, D Koes ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 253, 2017 | | 2017 |
| Characterization of Profilin Binding Kinetics using Ensemble Molecular Dynamics Simulations J Sunseri, DR Koes, P Roy, D Gau Biophysical Journal 112 (3), 287a, 2017 | | 2017 |
| GPU implementation of energy minimization for virtual screening J Sunseri, DR Koes ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 251, 2016 | | 2016 |
| Benchmarking computational methods for binding free-energy estimation J Sunseri, D Koes ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 251, 2016 | | 2016 |
| Supporting Information: GNINA 1.0: Molecular docking with deep learning A McNutt, P Francoeur, R Aggarwal, T Masuda, R Meli, M Ragoza, ... | | |
| Technical Details J Sunseri, DR Koes | | |
David KoesAssociate Professor, University of PittsburghVerified email at pitt.edu