Machine learning of chemical reactivity from databases of organic reactions

By Gonçalo V. S. M. Carrera, Sunil Gupta, João Aires-de-Sousa. 2009

18 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Environmental Chemistry, 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 Computational Theory and Mathematics

1. Mike Preuss
2. Marwin H. S. Segler
3. Mark P. Waller
4. Yuxi Li
5. A. Pais
6. Tânia F Cova
7. Sergey I. Nikolenko
8. Gisbert Schneider

In Environmental Chemistry

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

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): “Therefore, starting from reported reactions, negative reactions, for example with incorrect regio-and chemoselectivity were generated using an established protocol[40–42] together with negative sampling.”
• 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…”