Artem Cherkasov
Computational Theory and Mathematics · 2009–2022
A researcher in the Science corpus: 10 papers; trusts 75 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Computational Theory and Mathematics — 204 citations into it
- Molecular Biology — 153 citations into it
- Materials Chemistry — 113 citations into it
- Computational Chemistry & Drug Discovery — 78 citations into it
- Artificial Intelligence — 43 citations into it
- Pharmacology — 29 citations into it
- Computer Vision and Pattern Recognition — 25 citations into it
- Generative Models — 16 citations into it
Whom Artem Cherkasov trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Ilya Sutskever
- Alex Krizhevsky
- Yoshua Bengio
- Sergey Ioffe
- Oriol Vinyals
- Yann LeCun
- Dzmitry Bahdanau
- Max Welling
Who trusts Artem Cherkasov overall
- Anastasiia V. Sadybekov
- Garrett B. Goh
- Christopher D. Fjell
- Dongsheng Cao
- Josh Payne
- Rona R. Ramsay
- Robert E. W. Hancock
- Iván Olier
Papers from Artem Cherkasov's perspective
Ordered by how strongly the researchers Artem Cherkasov trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- QSAR Modeling: Where have you been? Where are you going to? (2014) — 1,811 citations
- QSAR without borders (2020) — 546 citations
- Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery (2020) — 348 citations
- All SMILES Variational Autoencoder (2019) — 43 citations
- Use of artificial intelligence in the design of small peptide antibiotics effective against a broad spectrum of highly antibiotic-resistant superbugs. (2009) — 349 citations
- Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information (2011) — 610 citations
- Rapid Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease by Deep Docking of 1.3 Billion Compounds (2020) — 442 citations
- Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking (2022) — 310 citations
- SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines (2017) — 463 citations
- The value of antimicrobial peptides in the age of resistance. (2020) — 759 citations