Building and Refining a Knowledge Base for Synthetic Organic Chemistry via the Methodology of Inductive and Deductive Machine Learning.

By H. Gelernter, J. Rose, Chyouhwa Chen. 1991

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. 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): “[55, 56, 22] The general advantages and limitations of automatic rule extraction have been discussed in detail elsewhere.”
• Neural Networks for the Prediction of Organic Chemistry Reactions (2016, unknown): “16 While these algorithms have their subtle di ff erences, all require a set of expert rules to predict reaction outcomes.”