The logic of chemical synthesis

By T. Huynh-Dinh. 1996

Subfields: Computational Chemistry & Drug Discovery

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Citations in the corpus

Planning chemical syntheses with deep neural networks and symbolic AI (2017, methodology): “[7, 3] Computer-assisted synthesis planning (CASP) could help chemists to ﬁnd better routes faster, and is a missing component in virtual de novo design and robot systems performing molecular design-synthesis-test cycles.”
Redox economy in organic synthesis. (2009, background): “”([72]) In order to have such a solution turn out to be redox-economic, four specifics should be considered and iteratively applied in such a cycle:”
Discovery and resupply of pharmacologically active plant-derived natural products: A review (2015, methodology): “This process is iterated until the synthons translate into readily available and inexpensive starting materials for the forward synthesis (Corey and Cheng, 1989).”
Computer‐aided synthesis design: 40 years on (2012, methodology): “11 In his essay, Corey also offered the ﬁrst concrete algorithm representing a strategy for conducting a logical synthetic analysis of a target molecule 2 : 1.”
Carbon–heteroatom bond formation catalysed by organometallic complexes (2008, methodology): “Although a framework has been established to plan synthetic sequence”
Click Chemistry: Diverse Chemical Function from a Few Good Reactions. (2001, background): “No expense or effort is spared to synthesize even minute quantities of these extraordinary molecules.[2, 3] The pharmaceutical industry, a direct scion of natural products chemistry,[4] has not been put off by difficult syntheses and many compounds being explored today as drug candidates represent s”
Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies (2017, methodology): “To make them, chemists usually plan their syntheses with a “working-backwards” problem-solving technique called retrosynthesis [Corey & Cheng (1989)].”
Emerging materials intelligence ecosystems propelled by machine learning (2020, background): “Formalized by Elias James Corey 207 , it is one of the most fundamental problems in organic chemistry and materials science (Fig.”