N. V. Dokholyan

Molecular Biology · 2006–2022

A researcher in the Science corpus: 23 papers; trusts 23 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 300 citations into it; #2,745 of 17,773 by trust here, #2,516 by citations
• Computational Theory and Mathematics — 149 citations into it; #5,707 of 12,182 by trust here, #2,104 by citations
• Materials Chemistry — 123 citations into it; #1,715 of 11,513 by trust here, #5,658 by citations
• Computational Chemistry & Drug Discovery — 66 citations into it
• Computational Biology & Bioinformatics — 38 citations into it
• Organic Chemistry — 25 citations into it
• Spectroscopy — 24 citations into it
• Natural Language Processing — 7 citations into it

Whom N. V. Dokholyan trusts overall

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

1. Feng Ding
2. Shuangye Yin
3. Martin Karplus
4. J. Desjarlais
5. Brian Kuhlman
6. Steven Henikoff
7. David Baker
8. S. Parasuraman
9. Marc-André Parisien
10. Roland L. Dunbrack

Who trusts N. V. Dokholyan overall

1. Feng Ding
2. Jian Wang
3. Shuangye Yin
4. Andrew Leaver-Fay
5. Srinivas Ramachandran
6. S. J. Macalino
7. Elizabeth A. Proctor
8. Ron Jacak

Papers from N. V. Dokholyan's perspective

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

1. Protein data bank
2. The Protein Data Bank
3. Blind tests of RNA nearest-neighbor energy prediction
4. De novo protein design: fully automated sequence selection.
5. Macromolecular modeling with rosetta.
6. Improved protein structure prediction using potentials from deep learning
7. Rosetta3: An Object-Oriented Software Suite for the Simulation and Design of Macromolecules
8. Comparative protein modelling by satisfaction of spatial restraints.
9. Atomic accuracy in predicting and designing non-canonical RNA structure
10. Quantitative analysis of nucleic acid three-dimensional structures.

Their papers

• Outcome of a Workshop on Applications of Protein Models in Biomedical Research (2009) — 145 citations
• Emergence of Protein Fold Families through Rational Design (2006) — 197 citations
• Eris: an automated estimator of protein stability (2007) — 404 citations
• RNA-Puzzles: a CASP-like evaluation of RNA three-dimensional structure prediction. (2012) — 150 citations
• RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures (2015) — 197 citations
• RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers (2020) — 141 citations
• iFoldRNA v2: folding RNA with constraints (2015) — 102 citations
• RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme (2017) — 197 citations
• MedusaScore: An Accurate Force-Field Based Scoring Function for Virtual Drug Screening (2008) — 201 citations
• Automated Minimization of Steric Clashes in Protein Structures (2011) — 649 citations
• Rapid flexible docking using a stochastic rotamer library of ligands (2010) — 97 citations
• Combined application of cheminformatics- and physical force field-based scoring functions improves binding affinity prediction for CSAR datasets (2011) — 22 citations
• Pharmacological Chaperones: Design and Development of New Therapeutic Strategies for the Treatment of Conformational Diseases. (2016) — 88 citations
• Predicting Protein–Ligand Docking Structure with Graph Neural Network (2022) — 61 citations
• Yuel: Compound-Protein Interaction Prediction with High Generalizability (2021)
• Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer’s Disease, Parkinson’s Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis (2021) — 544 citations
• Guiding Conventional Protein–Ligand Docking Software with Convolutional Neural Networks (2020) — 21 citations
• Allostery in Its Many Disguises: From Theory to Applications (2019) — 322 citations
• MedusaDock 2.0: Efficient and Accurate Protein-Ligand Docking With Constraints (2019) — 58 citations
• Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches (2013) — 22 citations