David Ryan Koes

Computational Theory and Mathematics · 2012–2021

A researcher in the Science corpus: 10 papers; trusts 44 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 103 citations into it; #2,244 of 12,182 by trust here, #996 by citations
• Molecular Biology — 98 citations into it; #2,371 of 17,773 by trust here, #5,550 by citations
• Computational Chemistry & Drug Discovery — 72 citations into it
• Materials Chemistry — 55 citations into it; #2,650 of 11,513 by trust here, #8,948 by citations
• Computer Vision and Pattern Recognition — 23 citations into it
• Artificial Intelligence — 22 citations into it
• Spectroscopy — 19 citations into it
• Pharmacology — 17 citations into it

Whom David Ryan Koes trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. John J. Irwin
2. Diederik P. Kingma
3. Patrick C. Maaß
4. Brian K. Shoichet
5. Renxiao Wang
6. Dina Schneidman-Duhovny
7. W. L. Jorgensen
8. David Weininger
9. Michael M. Mysinger
10. Anthony Nicholls

Who trusts David Ryan Koes overall

1. Jocelyn Sunseri
2. Hannes Stärk
3. R. Quiroga
4. Shuangjia Zheng
5. Maciej Wójcikowski
6. Izhar Wallach
7. Gabriele Corso
8. Mikhail Zaslavskiy
9. Octavian-Eugen Ganea
10. Shitong Luo

Papers from David Ryan Koes's perspective

Ordered by how strongly the researchers David Ryan Koes trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Long Short-Term Memory
4. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise (2013) — 1,027 citations
• 3Dmol.js: molecular visualization with WebGL (2014) — 334 citations
• Protein-Ligand Scoring with Convolutional Neural Networks (2016) — 313 citations
• GNINA 1.0: molecular docking with deep learning (2021) — 765 citations
• Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models (2020) — 61 citations
• ZINCPharmer: pharmacophore search of the ZINC database (2012) — 378 citations
• Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design (2020) — 242 citations
• Protein-Ligand Scoring with Convolutional Neural Networks (2017) — 465 citations
• Improvements to the APBS biomolecular solvation software suite (2017) — 2,172 citations
• Pharmit: interactive exploration of chemical space (2016) — 363 citations