Structure–reactivity modeling using mixture-based representation of chemical reactions

By Pavel G. Polishchuk, Timur I. Madzhidov, T. Gimadiev, A. Bodrov, R. Nugmanov, Alexandre Varnek. 2017

25 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Spectroscopy, 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 Materials Chemistry

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. Connor W. Coley

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.”