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AI-assisted Retrosynthetic Analysis
Synthetic organic chemistry involves in drug discovery and chemical biology. Retrosynthesis is one of the
most complex issues in the field of organic chemistry, which is the design of efficient synthetic routes for
a given target. It is an efficient and environmental-friendly synthesis of valuable molecules with well-
designed and feasible routes. Evidence show that similar products tend to be produced by similar
reactions (Reaxys or SciFinder). For general retrosynthesis planning, a proposed step can come from any
reaction class. Key considerations include the need to identify a cascade of disconnections schemes,
suitable building blocks and functional group protection strategies. Many additional considerations in
synthetic route planning are not limited to cost, process complexity, reaction yield, workup difficulty,
safety, and toxicity of intermediates.
Artificial intelligence (AI), driven by improved computing power, data availability and algorithms,
underpins chemical drug development. AI-assisted retrosynthetic analysis is starting from the target
compound and working backward, which has been well-reviewed over the years. AI approaches have
also been reported for prediction of reaction outcomes and optimization of reaction conditions. Potential
application of retrosynthetic program may play an important role in de novo molecular design and
automated synthesis of molecules.
Computer-aided Retrosynthetic Route Planning
The earlier retrosynthesis programs are mainly computer-aided retrosynthetic analysis tools. Template-
based methods for retrosynthetic analysis rely on human knowledge of organic synthesis, as well as the
encoding of organic and mechanistic rules.
Template-based, rule-based or similarity-based methods
Confirm the extent of generalization and abstraction.
Choose reaction databases.
Encode reaction templates or s