Route Designer: A Retrosynthetic Analysis Tool Utilizing Automated Retrosynthetic Rule Generation
By James Law, Z. Zsoldos, Anikó Simon, D. Reid, Yang Liu, Sing Yoong Khew, A. Peter Johnson, Sarah Major, R. A. Wade, H. Ando. 2009
207 citations recorded by the source corpus.
Subfields: Inorganic Chemistry, Molecular Biology, Organic Chemistry, 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 Molecular Biology
- Tommi S. Jaakkola
- Regina Barzilay
- Wengong Jin
- Marwin H. S. Segler
- Mark P. Waller
- Mike Preuss
- Connor W. Coley
- Bharath Ramsundar
In Organic Chemistry
In Computational Chemistry & Drug Discovery
- Marwin H. S. Segler
- Mark P. Waller
- Connor W. Coley
- Regina Barzilay
- Tommi S. Jaakkola
- Mike Preuss
- Wengong Jin
- Vijay S. Pande
Citations in the corpus
- Planning chemical syntheses with deep neural networks and symbolic AI (2017, methodology): “[55, 56, 22] The general advantages and limitations of automatic rule extraction have been discussed in detail elsewhere.”
- Computer‐aided synthesis design: 40 years on (2012, methodology): “In contrast to LHASA and SECS, the more recent ARChem (Route Designer) program 40 does not require generalized reaction transforms to be written in their entirety by skilled chemists using a special language.”
- Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network (2017, methodology): “The templates are currently either hand-crafted by experts [7, 1, 16] or generated from reaction databases with heuristic algorithms [2, 11, 3].”
- Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models (2017, methodology): “The rule extraction algorithm is adapted from Coley et al.’s implementation, 37 which was based on the algorithms described by Law et al. 20 and Bogevig et al. 52 For each atom-mapped reaction example in the training dataset, the reaction centers are extracted by identifying changes in connectivity…”
- Modelling Chemical Reasoning to Predict Reactions (2016, methodology): “In comparison, a transformation rule-based expert system as proposed by Christ et al. 41 et al. 42 resulted in a 52.7% accuracy for this validation set.”
- ReactionPredictor: Prediction of Complex Chemical Reactions at the Mechanistic Level Using Machine Learning (2012, background): “5,6 Other recent work in machine learning involves either narrowly scoped regioselectivity classi fi cation problems 16 or classifying the “ formability ” of bonds to guide a retro-synthetic search.”