Mining Electronic Laboratory Notebooks: Analysis, Retrosynthesis, and Reaction Based Enumeration

By Clara D. Christ, Matthias Zentgraf, Jan M. Kriegl. 2012

77 citations recorded by the source corpus.

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Planning chemical syntheses with deep neural networks and symbolic AI (2017, methodology): “[15–20] Approaches to algorithmically extract transformations from reaction datasets[21–23] on a purely symbolic level were criticized for their high amount of noise and lack of “chemical intelligence”.”
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].”
Computer-Assisted Retrosynthesis Based on Molecular Similarity (2017, unknown): “In contrast to traditional template extraction approaches that attempt to include neighboring atoms as necessary context, 12 − 15 these templates contain only the atoms that are immediately involved in the reaction (speci ﬁ ed by atomic identity, aromaticity, number of hydrogen atoms, and chirality…”
Artificial intelligence in drug discovery: recent advances and future perspectives (2021, background): “The majority of all known organic compounds can be synthesized with a limited number of robust reactions [162].”
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.”