Ron O. Dror

Molecular Biology · 2003–2023

A researcher in the Science corpus: 32 papers; trusts 76 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

Molecular Biology — 472 citations into it; #2,305 of 17,773 by trust here, #716 by citations
Materials Chemistry — 192 citations into it; #1,623 of 11,513 by trust here, #1,756 by citations
Cellular and Molecular Neuroscience — 111 citations into it; #2,811 of 2,998 by trust here, #287 by citations
Computational Theory and Mathematics — 109 citations into it; #5,957 of 12,182 by trust here, #1,587 by citations
Computational Biology & Bioinformatics — 73 citations into it; #551 of 660 by trust here, #484 by citations
Computational Chemistry & Drug Discovery — 69 citations into it
Atomic and Molecular Physics, and Optics — 62 citations into it
Spectroscopy — 59 citations into it

Whom Ron O. Dror trusts overall

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

Who trusts Ron O. Dror overall

Papers from Ron O. Dror's perspective

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

ImageNet classification with deep convolutional neural networks
Attention is All you Need (Neural Information Processing Systems)
ImageNet: A large-scale hierarchical image database
Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
Et al
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Long Short-Term Memory
Gradient-based learning applied to document recognition
Neural Message Passing for Quantum Chemistry (International Conference on Machine Learning)

Their papers

Parallel random numbers: As easy as 1, 2, 3 (2011) — 318 citations
How Fast-Folding Proteins Fold (2011) — 1,934 citations
Supporting Online Material Materials and Methods Figs. S1 to S10 Table S1 References Movies S1 to S3 Atomic-level Characterization of the Structural Dynamics of Proteins — 1,242 citations
Geometric deep learning of RNA structure (2021) — 339 citations
Learning from Protein Structure with Geometric Vector Perceptrons (2020) — 664 citations
Pathway and mechanism of drug binding to G-protein-coupled receptors (2011) — 717 citations
Improved side-chain torsion potentials for the Amber ff99SB protein force field (2010) — 5,790 citations
Systematic Validation of Protein Force Fields against Experimental Data (2012) — 659 citations
End-to-End Learning on 3D Protein Structure for Interface Prediction (2018) — 118 citations
Anton, a special-purpose machine for molecular dynamics simulation (2008) — 583 citations
Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer (2014) — 529 citations
Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes (2020) — 68 citations
Anton, a special-purpose machine for molecular dynamics simulation (2007) — 489 citations
Biomolecular simulation: a computational microscope for molecular biology. (2012) — 1,126 citations
Long-timescale molecular dynamics simulations of protein structure and function. (2009) — 744 citations
Structure and Function of an Irreversible Agonist-β2 Adrenoceptor complex (2011) — 766 citations
Molecular dynamics simulation for all (2018) — 2,691 citations
Structure and Dynamics of the M3 Muscarinic Acetylcholine Receptor (2012) — 789 citations
Structural and functional characterization of G protein–coupled receptors with deep mutational scanning (2019) — 140 citations
Activation mechanism of the β2-adrenergic receptor (2011) — 603 citations