Hannes Stärk

Molecular Biology · 2022–2025

A researcher in the Science corpus: 4 papers; trusts 24 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 104 citations into it; #8,587 of 17,773 by trust here, #10,047 by citations
• Computational Theory and Mathematics — 81 citations into it; #6,245 of 12,182 by trust here, #2,629 by citations
• Materials Chemistry — 61 citations into it
• Computational Chemistry & Drug Discovery — 27 citations into it; #232 of 425 by trust here, #281 by citations
• Computational Biology & Bioinformatics — 20 citations into it
• Artificial Intelligence — 12 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Model Architectures — 8 citations into it

Whom Hannes Stärk trusts overall

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

1. Diederik P. Kingma
2. Wen Torng
3. Max Welling
4. David Ryan Koes
5. Octavian-Eugen Ganea
6. Helen M. Berman
7. Jimmy Ba
8. Geoffrey E. Hinton
9. Raphael J. L. Townshend
10. Taco Cohen

Who trusts Hannes Stärk overall

1. Shuya Nakata
2. Gabriele Corso
3. Bowen Jing
4. Zhuoran Qiao
5. Yoshiharu Mori
6. Jiaqi Guan
7. Yuanqi Du
8. Petar Velickovic
9. Tommi S. Jaakkola
10. Regina Barzilay

Papers from Hannes Stärk's perspective

Ordered by how strongly the researchers Hannes Stärk 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. Attention is All you Need (Neural Information Processing Systems)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking (2022) — 790 citations
• EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022) — 380 citations
• Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery (2022) — 218 citations
• Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction (2025) — 481 citations