A robustness screen for the rapid assessment of chemical reactions

By Karl D. Collins, F. Glorius. 2013

467 citations recorded by the source corpus.

Subfields: Molecular Biology, Organic Chemistry, Computational Chemistry & Drug Discovery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Mike Preuss
2. Marwin H. S. Segler
3. Mark P. Waller
4. Sergey I. Nikolenko
5. Connor W. Coley
6. A. Pais
7. Tânia F Cova
8. Ola Engkvist

In Organic Chemistry

1. Mike Preuss
2. Mark P. Waller
3. Marwin H. S. Segler

In Computational Chemistry & Drug Discovery

1. Mike Preuss
2. Marwin H. S. Segler
3. Mark P. Waller
4. A. Pais
5. Tânia F Cova
6. Connor W. Coley
7. Philippe Schwaller
8. Teodoro Laino

Citations in the corpus

• Planning chemical syntheses with deep neural networks and symbolic AI (2017, methodology): “[7] A molecule failing to react in the way predicted by the transform is called ’out of scope’.”
• Modelling Chemical Reasoning to Predict Reactions (2016, methodology): “18,20,43 To obtain data of reactions which are known not to occur, we randomly selected 36000 known reactions from our validation set and generated ”wrong” (but still plausible) products by the application of 94 hand coded reaction rules, covering the most common chemical transformations, to the…”