G. de Fabritiis

Molecular Biology · 2009–2023

A researcher in the Science corpus: 12 papers; trusts 22 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 143 citations into it; #1,499 of 17,773 by trust here, #2,346 by citations
• Materials Chemistry — 103 citations into it; #1,213 of 11,513 by trust here, #3,040 by citations
• Computational Theory and Mathematics — 56 citations into it; #7,760 of 12,182 by trust here, #1,184 by citations
• Spectroscopy — 43 citations into it; #739 of 1,674 by trust here, #844 by citations
• Artificial Intelligence — 37 citations into it
• Computational Chemistry & Drug Discovery — 36 citations into it; #339 of 425 by trust here, #231 by citations
• Computer Vision and Pattern Recognition — 21 citations into it
• Optimization & Training — 8 citations into it

Whom G. de Fabritiis trusts overall

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

1. W. L. Jorgensen
2. M. Harvey
3. David P. Anderson
4. Diederik P. Kingma
5. Gregory R. Bowman
6. Marwin H. S. Segler
7. K. Lindorff‐Larsen
8. Sepp Hochreiter

Who trusts G. de Fabritiis overall

1. Frank No'e
2. John E. Stone
3. Hao Wu
4. John D. Chodera
5. Veronica Salmaso
6. Christoph Wehmeyer
7. Wei Wang
8. David J. Hardy
9. Fabian Paul
10. Hao Wu

Papers from G. de Fabritiis's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Long Short-Term Memory
4. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
5. ImageNet classification with deep convolutional neural networks
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. ImageNet: A large-scale hierarchical image database
10. Going deeper with convolutions

Their papers

• DeepSite: protein‐binding site predictor using 3D‐convolutional neural networks (2017) — 650 citations
• Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations (2011) — 617 citations
• KDEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks (2018) — 726 citations
• Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling (2017) — 317 citations
• HTMD: High-Throughput Molecular Dynamics for Molecular Discovery. (2016) — 416 citations
• ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. (2009) — 876 citations
• Shape-Based Generative Modeling for de Novo Drug Design (2019) — 208 citations
• Identification of slow molecular order parameters for Markov model construction. (2013) — 1,019 citations
• Coarse graining molecular dynamics with graph neural networks. (2020) — 219 citations
• An Implementation of the Smooth Particle Mesh Ewald Method on GPU Hardware. (2009) — 390 citations
• High-throughput molecular dynamics: the powerful new tool for drug discovery. (2012) — 97 citations
• OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials (2023) — 337 citations