Alexey Strokach

Molecular Biology · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 10 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 113 citations into it; #14,398 of 17,773 by trust here, #10,047 by citations
• Computational Biology & Bioinformatics — 54 citations into it
• Materials Chemistry — 41 citations into it
• Generative Models — 28 citations into it
• Spectroscopy — 15 citations into it

Whom Alexey Strokach trusts overall

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

1. David C. Jones
2. The Uniprot Consortium
3. Mark G. F. Sun
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Dong Xu
7. Albert Perez-Riba
8. Xinqiang Ding
9. Yoshua Bengio
10. Marc A. Martí-Renom

Who trusts Alexey Strokach overall

1. Namrata Anand
2. Zhangyang Gao
3. J. Trinquier
4. Ali Madani
5. Chloe Hsu
6. Ivan V. Anishchenko
7. J. Dauparas
8. Cheng Tan
9. Tudor Achim
10. Michael Jendrusch

Papers from Alexey Strokach's perspective

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

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

Their papers

• Fast and Flexible Protein Design Using Deep Graph Neural Networks. (2020) — 242 citations
• Fast and flexible design of novel proteins using graph neural networks (2019) — 17 citations
• Deep Generative Modeling for Protein Design (2021) — 129 citations