Andrew M. Watkins

Molecular Biology · 2014–2023

A researcher in the Science corpus: 13 papers; trusts 25 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 484 citations into it; #5,652 of 17,773 by trust here, #4,806 by citations
• Materials Chemistry — 213 citations into it
• Computational Biology & Bioinformatics — 98 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it
• Organic Chemistry — 16 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Computational Mechanics — 5 citations into it
• Physical and Theoretical Chemistry — 5 citations into it

Whom Andrew M. Watkins trusts overall

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

1. Rhiju Das
2. David Baker
3. Eric P. Nawrocki
4. Zhichao Miao
5. Mariusz Popenda
6. John Moult
7. Jessica S. Reuter
8. John Karanicolas
9. Robert M. Dirks
10. Michal J. Boniecki

Who trusts Andrew M. Watkins overall

1. Peng Xiong
2. Minkyung Baek
3. Rhiju Das
4. Raphael J. L. Townshend
5. Ruibo Wu
6. Ryan McHugh
7. Marcin Magnus
8. R. Rangan
9. Jian Zhan
10. Ivan V. Anishchenko

Papers from Andrew M. Watkins's perspective

Ordered by how strongly the researchers Andrew M. Watkins trusts endorse each paper. Same corpus, a different order for every researcher.

1. Atomic accuracy in predicting and designing non-canonical RNA structure
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. MolProbity: all-atom structure validation for macromolecular crystallography
4. ModeRNA: a tool for comparative modeling of RNA 3D structure
5. RNAstructure: software for RNA secondary structure prediction and analysis (BMC Bioinformatics)
6. The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data
7. Automated 3D structure composition for large RNAs
8. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
9. Attention is All you Need (Neural Information Processing Systems)
10. ImageNet classification with deep convolutional neural networks

Their papers

• Accurate de novo design of hyperstable constrained peptides (2016) — 389 citations
• Macromolecular modeling and design in Rosetta: recent methods and frameworks (2020) — 783 citations
• Geometric deep learning of RNA structure (2021) — 339 citations
• FARFAR2: Improved de novo Rosetta prediction of complex global RNA folds (2020) — 223 citations
• RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers (2020) — 141 citations
• Structure and ligand binding of the glutamine-II riboswitch (2019) — 21 citations
• Blind prediction of noncanonical RNA structure at atomic accuracy (2017) — 48 citations
• OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization (2023) — 435 citations
• Function-guided protein design by deep manifold sampling (2021) — 35 citations
• Designing Peptides on a Quantum Computer (2019) — 58 citations
• Better together: Elements of successful scientific software development in a distributed collaborative community (2020) — 37 citations
• Using Rosetta for RNA homology modeling (2019) — 16 citations
• Anatomy of β-Strands at Protein–Protein Interfaces (2014) — 101 citations