Computer‐Assisted Planning of Organic Syntheses: The Second Generation of Programs

By Wolf-Dietrich Ihlenfeldt, J. Gasteiger. 1996

Subfields: 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 Chemistry & Drug Discovery

1. Marwin H. S. Segler
2. Mark P. Waller
3. Gisbert Schneider
4. Mike Preuss
5. Peter Ertl
6. A. Peter Johnson
7. James Law
8. Anikó Simon

Citations in the corpus

• Planning chemical syntheses with deep neural networks and symbolic AI (2017, background): “[15, 13] Even complex molecules can be easily made in a few steps if there are readily available building blocks.”
• Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions (2009, methodology): “Gasteiger at Erlangen University with the WODCA system [5] or group of Prof.”
• Automating drug discovery (2017, background): “Classic approaches have been reviewed elsewhere 153–155 .”
• Computer‐aided synthesis design: 40 years on (2012, methodology): “25 Part of this lack of acceptance can be attributed to the chemist’s belief that they could do a better job than any computer program.”