Ivan V. Anishchenko

Molecular Biology · 2016–2023

A researcher in the Science corpus: 25 papers; trusts 72 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

Molecular Biology — 637 citations into it; #790 of 17,773 by trust here, #1,281 by citations
Computational Biology & Bioinformatics — 317 citations into it; #168 of 660 by trust here, #165 by citations
Materials Chemistry — 293 citations into it; #1,061 of 11,513 by trust here, #2,257 by citations
Computational Theory and Mathematics — 84 citations into it; #4,635 of 12,182 by trust here, #546 by citations
Computational Chemistry & Drug Discovery — 67 citations into it
Spectroscopy — 57 citations into it; #599 of 1,674 by trust here, #1,052 by citations
Artificial Intelligence — 35 citations into it
Evaluation & Benchmarks — 28 citations into it

Whom Ivan V. Anishchenko trusts overall

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

Who trusts Ivan V. Anishchenko overall

Papers from Ivan V. Anishchenko's perspective

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

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

Their papers

Improved protein structure prediction using predicted interresidue orientations (2019) — 1,274 citations
Prediction of homo- and hetero-protein complexes by protein docking and template-based modeling: a CASP-CAPRI experiment (2016) — 130 citations
De novo protein design by deep network hallucination (2020) — 559 citations
Computed structures of core eukaryotic protein complexes (2021) — 442 citations
Protein sequence design by conformational landscape optimization (2021) — 142 citations
Origins of coevolution between residues distant in protein 3D structures (2017) — 177 citations
Protein contact prediction using metagenome sequence data and residual neural networks (2019) — 79 citations
Deep learning methods for designing proteins scaffolding functional sites (2021) — 38 citations
Robust deep learning based protein sequence design using ProteinMPNN (2022) — 1,912 citations
Scaffolding protein functional sites using deep learning (2022) — 414 citations
Design of proteins presenting discontinuous functional sites using deep learning (2020) — 40 citations
Improved protein structure refinement guided by deep learning based accuracy estimation (2020) — 230 citations
Modeling complexes of modeled proteins (2017) — 27 citations
Dockground: A comprehensive data resource for modeling of protein complexes (2018) — 83 citations
Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. (2018) — 23 citations
Efficient and accurate prediction of protein structure using RoseTTAFold2 (2023) — 132 citations
ProteinGCN: Protein model quality assessment using Graph Convolutional Networks (2020) — 49 citations
Protein interaction networks revealed by proteome coevolution (2019) — 208 citations
Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA (2023) — 307 citations
Accurate prediction of nucleic acid and protein-nucleic acid complexes using RoseTTAFoldNA (2022) — 60 citations