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“Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models† †Electronic supplementary information (ESI) available: Time-split test set
TLDR
Using a text-based representation of molecules, chemical reactions are predicted with a neural machine translation model borrowed from language processing to describe how molecules behave in a graph-based model. Expand
Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds
TLDR
The largest available database of potentially exfoliable 2D materials has been obtained via high-throughput calculations using van der Waals density functional theory. Expand
Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction
TLDR
This work shows that a multihead attention Molecular Transformer model outperforms all algorithms in the literature, achieving a top-1 accuracy above 90% on a common benchmark data set and is able to handle inputs without a reactant–reagent split and including stereochemistry, which makes the method universally applicable. Expand
Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates
TLDR
It is shown that transfer learning of the general patent reaction model with a small set of carbohydrate reactions produces a specialized model returning predictions for carbohydrate reactions with remarkable accuracy. Expand
Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy†
TLDR
An extension of the Molecular Transformer model combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention is presented and the end-to-end framework has an excellent performance with few weaknesses related to the training data. Expand
Molecular Transformer for Chemical Reaction Prediction and Uncertainty Estimation
TLDR
This work treats reaction prediction as a machine translation problem between SMILES strings of reactants-reagents and the products, and shows that a multi-head attention Molecular Transformer model outperforms all algorithms in the literature, achieving a top-1 accuracy above 90% on a common benchmark dataset. Expand
Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery
TLDR
The impact made by natural language processing methodologies in the processing of spoken languages accelerated the application of NLP to elucidate hidden knowledge in textual representations of biochemical entities and then use it to construct models to predict molecular properties or to design novel molecules. Expand
Automated extraction of chemical synthesis actions from experimental procedures
TLDR
A deep-learning model based on the transformer architecture to translate experimental procedures from the field of organic chemistry into synthesis actions is developed and refined on manually annotated samples. Expand
Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design
TLDR
The problem of reagent prediction is discussed, in addition to reactant prediction, in order to solve the full synthesis design problem, highlighting the mismatch between what machine learning solves and what a lab chemist would need. Expand
Predicting retrosynthetic pathways using a combined linguistic model and hyper-graph exploration strategy
TLDR
This work introduces new metrics (coverage, class diversity, round-trip accuracy and Jensen-Shannon divergence) to evaluate the single-step retrosynthetic models, using the forward prediction and a reaction classification model always based on the transformer architecture. Expand
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