Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry

Aal E Ali, Rizvi Syed; Meng, Jiaolong; Khan, Muhammad Ehtisham Ibraheem; Jiang, Xuefeng

doi:10.1016/j.aichem.2024.100049

Cited by 8 publications

(2 citation statements)

References 162 publications

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“…Two key challenges exist: predicting the products that result from given reactants (i.e., substrates) and solving the reverse problem of identifying the reactants when the products are known. 6 One approach is the template-based method that extracts information from a reaction chemical database to generalize the application of existing reactions through reaction templates. 7 A template corresponds to subgraph patterns that describe changes in the connectivity between a product and its reactants.…”

Section: ■ Single-step Retrosynthesismentioning

confidence: 99%

“…Chemical reactions involve the transformation of one set of chemical substances into another, which results in chemical changes. Two key challenges exist: predicting the products that result from given reactants (i.e., substrates) and solving the reverse problem of identifying the reactants when the products are known . One approach is the template-based method that extracts information from a reaction chemical database to generalize the application of existing reactions through reaction templates .…”

Section: Single-step Retrosynthesismentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review

Gricourt,

Meyer,

Duigou

et al. 2024

ACS Synth. Biol.

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Retrosynthesis aims to efficiently plan the synthesis of desirable chemicals by strategically breaking down molecules into readily available building block compounds. Having a long history in chemistry, retro-biosynthesis has also been used in the fields of biocatalysis and synthetic biology. Artificial intelligence (AI) is driving us toward new frontiers in synthesis planning and the exploration of chemical spaces, arriving at an opportune moment for promoting bioproduction that would better align with green chemistry, enhancing environmental practices. In this review, we summarize the recent advancements in the application of AI methods and models for retrosynthetic and retrobiosynthetic pathway design. These techniques can be based either on reaction templates or generative models and require scoring functions and planning strategies to navigate through the retrosynthetic graph of possibilities. We finally discuss limitations and promising research directions in this field.

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Section: ■ Single-step Retrosynthesismentioning

confidence: 99%

Section: Single-step Retrosynthesismentioning

confidence: 99%