Jonathan Borowsky

Molecular Biology · 2022–2023

A researcher in the Science corpus: 2 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 44 citations into it
• Materials Chemistry — 18 citations into it
• Computational Biology & Bioinformatics — 16 citations into it
• Spectroscopy — 7 citations into it

Whom Jonathan Borowsky trusts overall

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

1. Wen Torng
2. Michele Parrinello
3. Giovanni Bussi
4. V. Guilloux
5. Russ B. Altman
6. S. Parasuraman
7. Vijay S. Pande
8. Minoru Kanehisa
9. Takuya Akiba

Papers from Jonathan Borowsky's perspective

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

1. Protein data bank
2. Highly accurate protein structure prediction with AlphaFold
3. 3D deep convolutional neural networks for amino acid environment similarity analysis
4. Design of a Novel Globular Protein Fold with Atomic-Level Accuracy
5. De novo design of a four-fold symmetric TIM-barrel protein with atomic-level accuracy
6. Accurate prediction of protein structures and interactions using a 3-track neural network (Science)
7. The Protein Data Bank
8. A point‐charge force field for molecular mechanics simulations of proteins based on condensed‐phase quantum mechanical calculations (Journal of Computational Chemistry)
9. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers
10. Polymorphic transitions in single crystals: A new molecular dynamics method

Their papers

• Predicting the locations of cryptic pockets from single protein structures using the PocketMiner graph neural network (2022) — 69 citations
• Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network (2023) — 109 citations