J. Dauparas
Molecular Biology · 2019–2023
A researcher in the Science corpus: 13 papers; trusts 15 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 293 citations into it
- Computational Biology & Bioinformatics — 187 citations into it
- Materials Chemistry — 154 citations into it
- Computational Chemistry & Drug Discovery — 57 citations into it
- Computational Theory and Mathematics — 31 citations into it
Whom J. Dauparas trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Hetunandan Kamisetty
- Ivan V. Anishchenko
- Sergey Ovchinnikov
- Florian Markowetz
- Martin Steinegger
- David T. Jones
- S. Dunn
- David Baker
- Magnus Ekeberg
- Valerio Mariani
Who trusts J. Dauparas overall
- Brian L. Trippe
- Casper A. Goverde
- Erik Nijkamp
- Luhuan Wu
- Zhuoran Qiao
- Kevin Kaichuang Yang
- Michael Heinzinger
- Kevin E. Wu
Papers from J. Dauparas's perspective
Ordered by how strongly the researchers J. Dauparas trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Long Short-Term Memory
- 5分で分かる!? 有名論文ナナメ読み:Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding
- Gradient-based learning applied to document recognition
- Rethinking the Inception Architecture for Computer Vision
- ImageNet: A large-scale hierarchical image database
Their papers
- Unified framework for modeling multivariate distributions in biological sequences (2019) — 11 citations
- Deep learning methods for designing proteins scaffolding functional sites (2021) — 38 citations
- Robust deep learning based protein sequence design using ProteinMPNN (2022) — 1,912 citations
- Scaffolding protein functional sites using deep learning (2022) — 414 citations
- Improved protein structure refinement guided by deep learning based accuracy estimation (2020) — 230 citations
- Hallucinating symmetric protein assemblies (2022) — 221 citations
- Improving de novo protein binder design with deep learning (2022) — 367 citations
- Hallucinating protein assemblies (2022) — 3 citations
- Mega-scale experimental analysis of protein folding stability in biology and design (2023) — 306 citations
- De novo design of luciferases using deep learning (2023) — 377 citations
- Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14 (2021) — 47 citations
- RoseTTAFold: The first release of RoseTTAFold (2021)
- Single Layers of Attention Suffice to Predict Protein Contacts (2020) — 35 citations