Bruno E. Correia

Molecular Biology · 2010–2022

A researcher in the Science corpus: 14 papers; trusts 39 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 498 citations into it; #16,851 of 17,773 by trust here, #4,586 by citations
• Materials Chemistry — 237 citations into it; #10,731 of 11,513 by trust here, #7,964 by citations
• Computational Theory and Mathematics — 123 citations into it; #11,911 of 12,182 by trust here, #1,500 by citations
• Computational Biology & Bioinformatics — 106 citations into it; #622 of 660 by trust here, #292 by citations
• Computational Chemistry & Drug Discovery — 74 citations into it
• Spectroscopy — 48 citations into it
• Computer Vision and Pattern Recognition — 45 citations into it
• Artificial Intelligence — 34 citations into it

Whom Bruno E. Correia trusts overall

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

1. Jean Feydy
2. Dina Schneidman-Duhovny
3. Sarah A. Teichmann
4. Benjamin Charlier
5. Matthias Fey
6. Freyr Sverrisson
7. Sankar Basu
8. Diederik P. Kingma
9. Michael M. Bronstein
10. Joan Alexis Glaunès

Who trusts Bruno E. Correia overall

1. I. Samish
2. Enrique Marcos
3. Freyr Sverrisson
4. Jue Wang
5. Daniel‐Adriano Silva
6. J. Bonet

Papers from Bruno E. Correia's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. ImageNet Large Scale Visual Recognition Challenge
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Macromolecular modeling and design in Rosetta: recent methods and frameworks (2020) — 783 citations
• Scaffolding protein functional sites using deep learning (2022) — 414 citations
• Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning (2019) — 804 citations
• De novo protein design by inversion of the AlphaFold structure prediction network (2022) — 83 citations
• Proteome-wide covalent ligand discovery in native biological systems (2016) — 775 citations
• Fast end-to-end learning on protein surfaces (2020) — 197 citations
• Rosetta FunFolDes – A general framework for the computational design of functional proteins (2018) — 36 citations
• Structure-based drug design with equivariant diffusion models (2022) — 455 citations
• Assessing and enhancing foldability in designed proteins (2021) — 17 citations
• Computational design of epitope-scaffolds allows induction of antibodies specific for a poorly immunogenic HIV vaccine epitope. (2010) — 223 citations
• P HYSICS - INFORMED DEEP NEURAL NETWORK FOR RIGID - BODY PROTEIN DOCKING (2022)
• Equivariant 3D-conditional diffusion model for molecular linker design (2022) — 284 citations
• Motif-Driven Design of Protein-Protein Interfaces. (2016) — 48 citations
• Proof of principle for epitope-focused vaccine design (2014) — 544 citations