Knowledge-Based Approach to de Novo Design Using Reaction Vectors

By Hina Patel, M. Bodkin, Beining Chen, Valerie J. Gillet. 2009

78 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, 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. Gisbert Schneider
2. Francesca Grisoni
3. José Jiménez-Luna
4. Marwin H. S. Segler
5. Mike Preuss
6. Nils Weskamp
7. Mark P. Waller
8. Markus Hartenfeller

In Materials Chemistry

1. Gisbert Schneider
2. Francesca Grisoni
3. Mike Preuss
4. José Jiménez-Luna
5. Marwin H. S. Segler
6. Nils Weskamp
7. Mark P. Waller
8. Markus Hartenfeller

In Molecular Biology

1. Gisbert Schneider
2. Francesca Grisoni
3. Marwin H. S. Segler
4. Mike Preuss
5. Mark P. Waller
6. Markus Hartenfeller
7. José Jiménez-Luna
8. Nils Weskamp

In Computational Chemistry & Drug Discovery

1. Marwin H. S. Segler
2. Gisbert Schneider
3. Mike Preuss
4. Francesca Grisoni
5. Mark P. Waller
6. José Jiménez-Luna
7. Nils Weskamp
8. Markus Hartenfeller

Citations in the corpus

• Planning chemical syntheses with deep neural networks and symbolic AI (2017, background): “The first branch embeds the reaction r i , represented as a ECFP4 reaction fingerprint[61–63, 39, 38] ̺ i modulo-folded to 2048 dimensions, via a single dense ELU layer.”
• Enabling future drug discovery by de novo design (2011, methodology): “The software tools SYNOPSIS, 58 DOGS, 80 and the reaction vector method described by Patel et al. 105 belong to this category.”
• Artificial intelligence in drug discovery: recent advances and future perspectives (2021, background): “These approaches are useful, for instance, to design synthetically accessible libraries [117,118], such as BI CLAIM [119] and CHIPMUNK [120].”