Wenhao Gao

Molecular Biology · 2020–2023

A researcher in the Science corpus: 5 papers; trusts 32 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 233 citations into it
• Materials Chemistry — 146 citations into it; #4,896 of 11,513 by trust here, #7,558 by citations
• Computational Theory and Mathematics — 142 citations into it; #6,948 of 12,182 by trust here, #3,796 by citations
• Artificial Intelligence — 135 citations into it
• Computational Biology & Bioinformatics — 69 citations into it
• Computational Chemistry & Drug Discovery — 55 citations into it
• Computer Vision and Pattern Recognition — 50 citations into it
• Generative Models — 44 citations into it

Whom Wenhao Gao trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Michael I. Jordan
4. Geoffrey E. Hinton
5. Yann LeCun
6. Alex Krizhevsky
7. Sheng Wang
8. Kaiming He
9. Radford M. Neal
10. Ilya Sutskever

Who trusts Wenhao Gao overall

1. Tianfan Fu
2. Freyr Sverrisson
3. Yuanqi Du
4. Kexin Huang
5. Shengchao Liu
6. Hanchen Wang
7. Jimeng Sun
8. Zhuoran Qiao
9. Jeffrey A. Ruffolo
10. Jean Feydy

Papers from Wenhao Gao's perspective

Ordered by how strongly the researchers Wenhao Gao 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. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Differentiable Scaffolding Tree for Molecular Optimization (2021) — 92 citations
• Deep Learning in Protein Structural Modeling and Design (2020) — 191 citations
• Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021) — 487 citations
• The Synthesizability of Molecules Proposed by Generative Models (2020) — 344 citations
• Scientific discovery in the age of artificial intelligence (2023) — 1,745 citations