Computer-Assisted Synthetic Planning: The End of the Beginning.

By Sara Szymkuć, E. P. Gajewska, T. Klucznik, K. Molga, P. Dittwald, M. Startek, M. Bajczyk, B. Grzybowski. 2016

497 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Pharmacology, 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. Sepp Hochreiter
2. Günter Klambauer
3. Mike Preuss
4. Philipp Renz
5. Marwin H. S. Segler
6. Jörg Kurt Wegner
7. Philipp Seidl
8. Paulo Neves

In Materials Chemistry

1. Sepp Hochreiter
2. Philipp Renz
3. Günter Klambauer
4. Mike Preuss
5. Marwin H. S. Segler
6. Keith T. Butler
7. Aron Walsh
8. Olexandr Isayev

In Pharmacology

1. Günter Klambauer
2. Sepp Hochreiter
3. Jörg Kurt Wegner

In Computational Chemistry & Drug Discovery

1. Sepp Hochreiter
2. Günter Klambauer
3. Mike Preuss
4. Philipp Renz
5. Marwin H. S. Segler
6. Jörg Kurt Wegner
7. Olexandr Isayev
8. Philipp Seidl

Citations in the corpus

• Planning chemical syntheses with deep neural networks and symbolic AI (2017, methodology): “[15, 13] Even complex molecules can be easily made in a few steps if there are readily available building blocks.”
• Modern Hopfield Networks for Few- and Zero-Shot Reaction Prediction (2021, methodology): “To aid in this complex task, chemists have resorted to computer-assisted synthesis planning (CASP) methods to support their discovery of new synthesis paths (Corey & Wipke, 1969; Szymkuć et al., 2016).”
• Prediction of Organic Reaction Outcomes Using Machine Learning (2017, unknown): “While this is a more scalable approach than manually listing incompatible groups, 7 it has not been adapted into a directly translatable template-improvement algorithm.”