Ola Engkvist

Computational Theory and Mathematics · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 55 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 133 citations into it
• Materials Chemistry — 92 citations into it; #1,427 of 11,513 by trust here, #2,967 by citations
• Molecular Biology — 89 citations into it
• Computational Chemistry & Drug Discovery — 77 citations into it; #128 of 425 by trust here, #163 by citations
• Artificial Intelligence — 66 citations into it; #11,955 of 35,345 by trust here, #8,810 by citations
• Computer Vision and Pattern Recognition — 46 citations into it
• Generative Models — 41 citations into it
• Model Architectures — 24 citations into it

Whom Ola Engkvist trusts overall

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

1. Diederik P. Kingma
2. Sepp Hochreiter
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. J. Urgen Schmidhuber
6. Yann LeCun
7. Marwin H. S. Segler
8. Ronald J. Williams
9. Esben Jannik Bjerrum
10. Alex Krizhevsky

Who trusts Ola Engkvist overall

1. Sergey I. Nikolenko
2. Jiaxuan You
3. Rohan Gupta
4. Mengying Sun
5. Antonio Serrano
6. Tânia F Cova
7. Jaechang Lim
8. Kit-Kay Mak
9. Wengong Jin
10. Kristina Preuer

Papers from Ola Engkvist's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Molecular de-novo design through deep reinforcement learning (2017) — 1,267 citations
• Application of Generative Autoencoder in De Novo Molecular Design (2017) — 375 citations
• The rise of deep learning in drug discovery. (2018) — 1,560 citations
• A de novo molecular generation method using latent vector based generative adversarial network (2019) — 356 citations
• Randomized SMILES strings improve the quality of molecular generative models (2019) — 340 citations
• AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning (2020) — 333 citations
• Molecular representations in AI-driven drug discovery: a review and practical guide (2020) — 482 citations