Pablo Gainza

Molecular Biology · 2013–2019

A researcher in the Science corpus: 3 papers; trusts 11 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 50 citations into it; #14,698 of 17,773 by trust here, #8,570 by citations
• Materials Chemistry — 18 citations into it
• Computational Theory and Mathematics — 9 citations into it; #11,003 of 12,182 by trust here, #2,851 by citations
• Spectroscopy — 6 citations into it

Whom Pablo Gainza trusts overall

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

1. Michael D. Tyka
2. Amelie Stein
3. Daniel J. Mandell
4. Hahnbeom Park
5. David Baker
6. Alexander M. Sevy
7. Tanja Kortemme
8. Patrick Conway
9. Po-Ssu Huang
10. David A. Sontag

Who trusts Pablo Gainza overall

1. Freyr Sverrisson
2. Marinka Zitnik
3. Michelle M. Li
4. Jérôme Tubiana
5. Vignesh Ram Somnath
6. Alex Morehead
7. Emanuele Rossi
8. Wenhao Gao
9. Bruno E. Correia
10. Casper A. Goverde

Papers from Pablo Gainza's perspective

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

1. Efficient Sampling of Protein Conformational Space Using Fast Loop Building and Batch Minimization on Highly Parallel Computers
2. Alternate states of proteins revealed by detailed energy landscape mapping
3. Design of a Novel Globular Protein Fold with Atomic-Level Accuracy
4. Improvements to Robotics-Inspired Conformational Sampling in Rosetta
5. Sub-angstrom accuracy in protein loop reconstruction by robotics-inspired conformational sampling
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Gradient-based learning applied to document recognition
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet classification with deep convolutional neural networks
10. Computational design of proteins targeting the conserved stem region of influenza hemagglutinin

Their papers

• Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning (2019) — 804 citations
• Algorithms for Protein Design (2016) — 73 citations
• OSPREY: protein design with ensembles, flexibility, and provable algorithms. (2013)