Expert System for Predicting Reaction Conditions: The Michael Reaction Case

By G. Marcou, João Aires-de-Sousa, Diogo Latino, A. D. Luca, D. Horvath, V. Rietsch, Alexandre Varnek. 2015

73 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Spectroscopy, Knowledge Graphs & Representation, 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 Artificial Intelligence

1. Marwin H. S. Segler
2. Mike Preuss
3. Mark P. Waller
4. Frank Hutter
5. Ilya Loshchilov
6. Holger H. Hoos
7. A. Pais
8. Tânia F Cova

In Spectroscopy

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, background): “[38, 39] The classifier has to be trained on successful and failed reactions.”
• Modelling Chemical Reasoning to Predict Reactions (2016, methodology): “20 • Baldi and coworkers reported an elegant approach to make predictions at the mechanistic level for reactions not involving transition metal catalysis.”