Jun Ma

Molecular Biology · with Sun · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 13 citations into it

Whom Jun Ma trusts overall

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

1. Roland L. Dunbrack

Papers from Jun Ma's perspective

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

1. The penultimate rotamer library
2. Human-level control through deep reinforcement learning
3. Protein Structure Prediction Using Rosetta
4. Improved methods for building protein models in electron density maps and the location of errors in these models.
5. Prediction of protein side-chain rotamers from a backbone-dependent rotamer library: a new homology modeling tool.
6. Deep Learning
7. Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. (Journal of Molecular Biology)
8. Rotamer libraries in the 21st century.
9. The Uppsala Electron-Density Server.
10. A graph‐theory algorithm for rapid protein side‐chain prediction (Protein Science)

Their papers

• Prediction of amino acid side chain conformation using a deep neural network (2017) — 17 citations
• Chemi-net: a graph convolutional network for accurate drug property prediction (2018) — 31 citations