scholarly article | Q13442814 |
P819 | ADS bibcode | 2020NatCo..11.5575T |
P818 | arXiv ID | 2003.02804 |
P356 | DOI | 10.1038/S41467-020-19266-Y |
P698 | PubMed publication ID | 33149154 |
P50 | author | Guillaume Godin | Q101226413 |
Igor V. Tetko | Q30362344 | ||
Ruud Van Deursen | Q101226412 | ||
P2093 | author name string | Pavel Karpov | |
P2860 | cites work | Transformer-CNN: Swiss knife for QSAR modeling and interpretation | Q102131148 |
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Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction | Q90403932 | ||
"Found in Translation": predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models | Q90843756 | ||
Prediction and Interpretable Visualization of Retrosynthetic Reactions Using Graph Convolutional Networks | Q91461012 | ||
Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis | Q91531144 | ||
Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks | Q91876087 | ||
QSAR without borders | Q94477921 | ||
Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning | Q96615446 | ||
P433 | issue | 1 | |
P921 | main subject | transformer | Q11658 |
P304 | page(s) | 5575 | |
P577 | publication date | 2020-11-04 | |
P1433 | published in | Nature Communications | Q573880 |
P1476 | title | State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis | |
P478 | volume | 11 |
Q108812226 | Extraction of organic chemistry grammar from unsupervised learning of chemical reactions |
Q104412579 | From Big Data to Artificial Intelligence: chemoinformatics meets new challenges |
Q120967988 | Transformer-based artificial neural networks for the conversion between chemical notations |
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